Date: (Sat) Jun 11, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
    debugSource("~/Dropbox/datascience/R/mydsutils.R") else
    source("~/Dropbox/datascience/R/mydsutils.R")    
## Loading required package: caret
## Loading required package: lattice
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- #NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
'(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     
# chk ref value against frequencies vs. alpha sort order
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D")) 
    
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
# glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)

        # retVal <- rep_len(0, length(raw))
        stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
        stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0) 
        # msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
        # msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
        # msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
        # msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
        # msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
        # msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
        # msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
        # msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65

        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)        
        retVal <- sapply(raw, function(age) {
            if (is.na(age)) return(0) else
            if ((age > 15) && (age <= 20)) return(age - 15) else
            if ((age > 20) && (age <= 25)) return(age - 20) else
            if ((age > 25) && (age <= 30)) return(age - 25) else
            if ((age > 30) && (age <= 35)) return(age - 30) else
            if ((age > 35) && (age <= 40)) return(age - 35) else
            if ((age > 40) && (age <= 50)) return(age - 40) else
            if ((age > 50) && (age <= 65)) return(age - 50) else
            if ((age > 65) && (age <= 90)) return(age - 65)
        })
        
        return(retVal)
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr", 
                     # # "Hhold.fctr",
                     # "Edn.fctr",
                     # paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(96, 112, 120, 124, 128, 129, 130, 131, 132, 133, 135, 138, 142, 157, 187, 247) # accuracy(131) = 0.6285
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164

glbRFEResults <- NULL

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
#   RFE = "Recursive Feature Elimination"
#   Csm = CuStoM
#   NOr = No OutlieRs
#   Inc = INteraCt
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") 
} else {
    glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
    # glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
    #     , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
    #     , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
    #     , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
    #     , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
    #     , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !     
    #     , "svmRadial" # didn't bother
    #     ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
    #                                     ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
# glmnetTuneParams <- rbind(data.frame()
#                         ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
#                         ,data.frame(parameter = "lambda", vals = "9.342e-02")    
#                         )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
bagEarthTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "degree", vals = "1")
                        ,data.frame(parameter = "nprune", vals = "256")
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
#                                      bagEarthTuneParams))

# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)

pkgPreprocMethods <-     
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
#   Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
    c(NULL
      ,"zv", "nzv"
      ,"BoxCox", "YeoJohnson", "expoTrans"
      ,"center", "scale", "center.scale", "range"
      ,"knnImpute", "bagImpute", "medianImpute"
      ,"zv.pca", "ica", "spatialSign"
      ,"conditionalX") 

glbMdlPreprocMethods <- list(NULL # : default
    # "All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods, 
    #                                           c("knnImpute", "bagImpute", "medianImpute")),
    #                                 # NULL))
    #                                 c("nzv.spatialSign")))    
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
#                                                     "nzv.pca.spatialSign"))

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
                           "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
              "min.elapsedtime.everything", 
              # "min.aic.fit", 
              "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL #"auto"
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- # NULL #: default
    c("Votes_Ensemble_cnk06_out_fin.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Q109244No_AllX_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- NULL # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # NULL # default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- NULL # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##         label step_major step_minor label_minor   bgn end elapsed
## 1 import.data          1          0           0 6.566  NA      NA

Step 1.0: import data

chunk option: eval=

## [1] "Reading file ./data/train2016.csv..."
## [1] "dimensions of data in ./data/train2016.csv: 5,568 rows x 108 cols"
##   USER_ID  YOB Gender              Income            HouseholdStatus
## 1       1 1938   Male                               Married (w/kids)
## 2       4 1970 Female       over $150,000 Domestic Partners (w/kids)
## 3       5 1997   Male  $75,000 - $100,000           Single (no kids)
## 4       8 1983   Male $100,001 - $150,000           Married (w/kids)
## 5       9 1984 Female   $50,000 - $74,999           Married (w/kids)
## 6      10 1997 Female       over $150,000           Single (no kids)
##        EducationLevel      Party Q124742 Q124122 Q123464 Q123621 Q122769
## 1                       Democrat      No              No      No      No
## 2   Bachelor's Degree   Democrat             Yes      No      No      No
## 3 High School Diploma Republican             Yes     Yes      No        
## 4   Bachelor's Degree   Democrat      No     Yes      No     Yes      No
## 5 High School Diploma Republican      No     Yes      No      No      No
## 6        Current K-12   Democrat                                      No
##   Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1     Yes  Public      No     Yes      No              No      No     Yes
## 2     Yes  Public      No     Yes      No     Yes      No      No     Yes
## 3     Yes Private      No      No      No     Yes      No      No     Yes
## 4      No  Public      No     Yes      No     Yes      No      No     Yes
## 5     Yes  Public      No     Yes      No     Yes     Yes      No     Yes
## 6     Yes  Public      No      No      No     Yes      No     Yes     Yes
##   Q120472     Q120194 Q120012 Q120014 Q119334 Q119851   Q119650 Q118892
## 1           Try first      No      No             Yes               Yes
## 2 Science Study first     Yes     Yes      No      No Receiving      No
## 3 Science Study first             Yes      No     Yes Receiving      No
## 4 Science   Try first      No     Yes     Yes      No    Giving     Yes
## 5     Art   Try first     Yes      No      No      No    Giving      No
## 6 Science   Try first     Yes     Yes      No     Yes Receiving      No
##   Q118117    Q118232 Q118233 Q118237     Q117186        Q117193 Q116797
## 1     Yes   Idealist      No      No                                Yes
## 2      No Pragmatist      No      No Cool headed Standard hours      No
## 3     Yes Pragmatist      No     Yes Cool headed      Odd hours      No
## 4      No   Idealist      No      No Cool headed Standard hours      No
## 5      No   Idealist     Yes     Yes  Hot headed Standard hours      No
## 6      No Pragmatist      No      No             Standard hours        
##   Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1   Happy     Yes     Yes      No      No    P.M.     Yes   Start     Yes
## 2   Happy     Yes     Yes     Yes      No    A.M.      No     End     Yes
## 3   Right     Yes      No      No     Yes    A.M.     Yes   Start     Yes
## 4   Happy     Yes     Yes      No      No    A.M.     Yes   Start     Yes
## 5   Happy     Yes     Yes      No     Yes    P.M.      No     End      No
## 6                                                                        
##   Q115611       Q115899 Q115390 Q114961 Q114748 Q115195 Q114517    Q114386
## 1      No Circumstances     Yes     Yes     Yes     Yes      No           
## 2      No            Me     Yes     Yes      No     Yes      No Mysterious
## 3     Yes Circumstances      No     Yes      No     Yes     Yes Mysterious
## 4      No Circumstances     Yes      No      No     Yes      No        TMI
## 5      No            Me      No     Yes     Yes     Yes     Yes        TMI
## 6                                                                         
##   Q113992 Q114152 Q113583    Q113584 Q113181 Q112478 Q112512 Q112270
## 1     Yes     Yes    Talk Technology      No      No     Yes        
## 2      No      No                                                   
## 3      No      No   Tunes Technology     Yes     Yes     Yes     Yes
## 4      No      No    Talk     People      No     Yes     Yes     Yes
## 5     Yes      No   Tunes     People      No      No     Yes      No
## 6                                                                   
##   Q111848    Q111580 Q111220 Q110740 Q109367       Q108950 Q109244 Q108855
## 1      No  Demanding      No              No      Cautious      No    Yes!
## 2                                Mac     Yes      Cautious      No  Umm...
## 3      No Supportive      No      PC      No      Cautious      No  Umm...
## 4     Yes Supportive      No     Mac     Yes Risk-friendly      No  Umm...
## 5      No  Demanding     Yes      PC     Yes      Cautious      No    Yes!
## 6     Yes Supportive      No      PC                                      
##   Q108617   Q108856 Q108754   Q108342 Q108343 Q107869 Q107491 Q106993
## 1      No     Space      No In-person             Yes      No     Yes
## 2      No     Space     Yes In-person      No     Yes     Yes      No
## 3      No     Space      No In-person      No      No     Yes     Yes
## 4      No Socialize     Yes    Online      No     Yes      No     Yes
## 5      No Socialize      No    Online      No      No     Yes     Yes
## 6                           In-person      No      No     Yes     Yes
##       Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1 Yay people!     Yes      No     Yes     Yes              No     Yes
## 2 Yay people!     Yes     Yes     Yes     Yes     Yes      No     Yes
## 3 Grrr people     Yes      No      No      No      No      No      No
## 4 Grrr people      No      No     Yes     Yes      No     Yes     Yes
## 5 Yay people!     Yes      No     Yes     Yes     Yes     Yes      No
## 6 Grrr people     Yes      No     Yes     Yes      No      No     Yes
##   Q103293 Q102906 Q102674 Q102687 Q102289 Q102089   Q101162 Q101163
## 1      No      No      No     Yes      No     Own  Optimist        
## 2                                                                  
## 3     Yes      No      No     Yes      No     Own Pessimist     Mom
## 4      No      No      No     Yes     Yes     Own  Optimist     Mom
## 5      No      No     Yes      No      No     Own  Optimist     Mom
## 6     Yes     Yes      No     Yes                                  
##   Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1     Yes     Yes      No      No   Nope     Yes     No     No       
## 2                                                                  No
## 3      No      No      No      No   Nope     Yes     No     No     No
## 4      No      No      No     Yes Check!      No     No     No    Yes
## 5      No     Yes     Yes     Yes   Nope     Yes     No     No    Yes
## 6                                                                    
##   Q98869 Q98578     Q98059 Q98078 Q98197 Q96024
## 1     No        Only-child     No     No    Yes
## 2     No     No Only-child    Yes     No     No
## 3    Yes     No        Yes     No    Yes     No
## 4    Yes     No        Yes     No     No    Yes
## 5     No     No        Yes     No     No    Yes
## 6                                              
##      USER_ID  YOB Gender              Income             HouseholdStatus
## 193      245 1964   Male       over $150,000            Married (w/kids)
## 848     1046 1953   Male $100,001 - $150,000 Domestic Partners (no kids)
## 2836    3530 1995   Male                                Single (no kids)
## 4052    5050 1945 Female  $75,000 - $100,000            Married (w/kids)
## 4093    5107 1980 Female $100,001 - $150,000            Married (w/kids)
## 5509    6888 1998 Female       under $25,000            Single (no kids)
##             EducationLevel      Party Q124742 Q124122 Q123464 Q123621
## 193      Bachelor's Degree Republican     Yes     Yes      No     Yes
## 848                          Democrat                                
## 2836 Current Undergraduate   Democrat     Yes     Yes     Yes      No
## 4052     Bachelor's Degree Republican                                
## 4093     Bachelor's Degree   Democrat                      No      No
## 5509          Current K-12 Republican                                
##      Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 193       No     Yes  Public      No     Yes      No     Yes      No
## 848                                                                 
## 2836             Yes  Public     Yes      No      No     Yes     Yes
## 4052              No  Public                                        
## 4093      No      No Private      No                                
## 5509                                                     Yes     Yes
##      Q120379 Q120650 Q120472     Q120194 Q120012 Q120014 Q119334 Q119851
## 193       No     Yes Science   Try first     Yes     Yes     Yes      No
## 848                                                                     
## 2836     Yes     Yes     Art Study first      No     Yes             Yes
## 4052                                                                    
## 4093                                                         Yes        
## 5509     Yes      No     Art Study first     Yes      No     Yes      No
##      Q119650 Q118892 Q118117    Q118232 Q118233 Q118237     Q117186
## 193   Giving     Yes      No   Idealist     Yes     Yes  Hot headed
## 848                                                                
## 2836             Yes     Yes   Idealist     Yes      No Cool headed
## 4052                      No                 No      No            
## 4093              No      No Pragmatist      No     Yes            
## 5509  Giving      No                                               
##             Q117193 Q116797 Q116881 Q116953 Q116601 Q116441 Q116448
## 193  Standard hours      No   Happy     Yes     Yes      No      No
## 848                                                                
## 2836      Odd hours      No   Happy     Yes     Yes              No
## 4052                                                               
## 4093                                                               
## 5509                                                               
##      Q116197 Q115602 Q115777 Q115610 Q115611       Q115899 Q115390 Q114961
## 193     A.M.     Yes     End     Yes     Yes            Me      No      No
## 848                                                                       
## 2836             Yes     End     Yes      No Circumstances     Yes      No
## 4052    P.M.     Yes   Start     Yes      No                    No        
## 4093    P.M.     Yes   Start     Yes      No Circumstances                
## 5509                                                                      
##      Q114748 Q115195 Q114517    Q114386 Q113992 Q114152 Q113583    Q113584
## 193      Yes      No     Yes        TMI      No     Yes   Tunes Technology
## 848                                                                       
## 2836     Yes      No      No Mysterious      No     Yes   Tunes     People
## 4052      No     Yes                                                      
## 4093                                                      Tunes     People
## 5509                                                                      
##      Q113181 Q112478 Q112512 Q112270 Q111848    Q111580 Q111220 Q110740
## 193       No     Yes             Yes     Yes Supportive      No     Mac
## 848                                                                    
## 2836     Yes     Yes     Yes      No     Yes  Demanding     Yes      PC
## 4052                                                                   
## 4093                                     Yes Supportive                
## 5509                                                                   
##      Q109367       Q108950 Q109244 Q108855 Q108617   Q108856 Q108754
## 193       No      Cautious      No    Yes!      No Socialize      No
## 848      Yes Risk-friendly     Yes    Yes!      No     Space      No
## 2836     Yes      Cautious     Yes             Yes                  
## 4052                                                                
## 4093      No Risk-friendly      No    Yes!      No     Space      No
## 5509                                                                
##        Q108342 Q108343 Q107869 Q107491 Q106993     Q106997 Q106272 Q106388
## 193  In-person      No     Yes     Yes      No Yay people!     Yes     Yes
## 848  In-person     Yes                                                    
## 2836 In-person     Yes             Yes                         Yes      No
## 4052                                        No Grrr people                
## 4093 In-person     Yes     Yes     Yes     Yes Yay people!     Yes     Yes
## 5509                                                                      
##      Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906 Q102674
## 193       No     Yes      No      No     Yes      No      No      No
## 848                                                                 
## 2836     Yes      No      No      No     Yes     Yes      No      No
## 4052                              No      No      No              No
## 4093      No      No      No      No     Yes      No      No     Yes
## 5509                                                                
##      Q102687 Q102289 Q102089  Q101162 Q101163 Q101596 Q100689 Q100680
## 193       No      No     Own Optimist     Dad     Yes     Yes      No
## 848                                                                  
## 2836     Yes     Yes    Rent Optimist     Dad      No     Yes     Yes
## 4052     Yes             Own                       No                
## 4093     Yes     Yes    Rent                               No     Yes
## 5509                                                                 
##      Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 193      Yes Check!      No     No     No    Yes    Yes     No    Yes
## 848                                                                  
## 2836     Yes Check!      No     No     No    Yes    Yes           Yes
## 4052                                                                 
## 4093      No   Nope     Yes     No    Yes    Yes    Yes     No    Yes
## 5509                                                                 
##      Q98078 Q98197 Q96024
## 193      No    Yes    Yes
## 848                    No
## 2836    Yes    Yes     No
## 4052                     
## 4093    Yes    Yes     No
## 5509                     
##      USER_ID  YOB Gender            Income  HouseholdStatus
## 5563    6955 1966   Male     over $150,000 Married (w/kids)
## 5564    6956   NA   Male                                   
## 5565    6957 2000 Female                                   
## 5566    6958 1969   Male     over $150,000                 
## 5567    6959 1986   Male $25,001 - $50,000 Married (w/kids)
## 5568    6960 1999   Male     under $25,000 Single (no kids)
##           EducationLevel      Party Q124742 Q124122 Q123464 Q123621
## 5563   Bachelor's Degree   Democrat                                
## 5564     Master's Degree   Democrat              No      No        
## 5565        Current K-12 Republican                                
## 5566   Bachelor's Degree   Democrat                             Yes
## 5567 High School Diploma Republican                                
## 5568        Current K-12 Republican                                
##      Q122769 Q122770 Q122771 Q122120 Q121699 Q121700 Q120978 Q121011
## 5563                              No     Yes      No     Yes     Yes
## 5564      No     Yes  Public             Yes                        
## 5565                  Public                             Yes        
## 5566                              No      No      No     Yes     Yes
## 5567                             Yes             Yes              No
## 5568                                     Yes      No      No        
##      Q120379 Q120650 Q120472   Q120194 Q120012 Q120014 Q119334 Q119851
## 5563                                                                  
## 5564                                                                  
## 5565     Yes     Yes     Art Try first      No     Yes     Yes     Yes
## 5566     Yes     Yes Science                                          
## 5567      No      No Science                No     Yes                
## 5568                                                                  
##        Q119650 Q118892 Q118117 Q118232 Q118233 Q118237 Q117186 Q117193
## 5563                                                                  
## 5564                                                                  
## 5565 Receiving                                                        
## 5566                                                                  
## 5567                                                                  
## 5568                                                                  
##      Q116797 Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q115777 Q115610 Q115611 Q115899 Q115390 Q114961 Q114748 Q115195
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q114517 Q114386 Q113992 Q114152 Q113583 Q113584 Q113181 Q112478
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q112512 Q112270 Q111848 Q111580 Q111220 Q110740 Q109367 Q108950
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q109244 Q108855 Q108617 Q108856 Q108754 Q108342 Q108343 Q107869
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q107491 Q106993 Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 5563                                                                
## 5564                                                                
## 5565                                                                
## 5566                                                                
## 5567                                                                
## 5568                                                                
##      Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716
## 5563                                                                      
## 5564                                                                      
## 5565                                                                      
## 5566                                                                      
## 5567                                                                      
## 5568                                                                      
##      Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 5563                                                        
## 5564                                                        
## 5565                                                        
## 5566                                                        
## 5567                                                        
## 5568                                                        
## 'data.frame':    5568 obs. of  20 variables:
##  $ USER_ID        : int  1 4 5 8 9 10 11 12 13 15 ...
##  $ YOB            : int  1938 1970 1997 1983 1984 1997 1983 1996 NA 1981 ...
##  $ Gender         : chr  "Male" "Female" "Male" "Male" ...
##  $ Income         : chr  "" "over $150,000" "$75,000 - $100,000" "$100,001 - $150,000" ...
##  $ HouseholdStatus: chr  "Married (w/kids)" "Domestic Partners (w/kids)" "Single (no kids)" "Married (w/kids)" ...
##  $ EducationLevel : chr  "" "Bachelor's Degree" "High School Diploma" "Bachelor's Degree" ...
##  $ Party          : chr  "Democrat" "Democrat" "Republican" "Democrat" ...
##  $ Q124742        : chr  "No" "" "" "No" ...
##  $ Q124122        : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q123464        : chr  "No" "No" "Yes" "No" ...
##  $ Q123621        : chr  "No" "No" "No" "Yes" ...
##  $ Q122769        : chr  "No" "No" "" "No" ...
##  $ Q122770        : chr  "Yes" "Yes" "Yes" "No" ...
##  $ Q122771        : chr  "Public" "Public" "Private" "Public" ...
##  $ Q122120        : chr  "No" "No" "No" "No" ...
##  $ Q121699        : chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q121700        : chr  "No" "No" "No" "No" ...
##  $ Q120978        : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q121011        : chr  "No" "No" "No" "No" ...
##  $ Q120379        : chr  "No" "No" "No" "No" ...
## NULL
## 'data.frame':    5568 obs. of  20 variables:
##  $ Q120650: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q118117: chr  "Yes" "No" "Yes" "No" ...
##  $ Q118233: chr  "No" "No" "No" "No" ...
##  $ Q118237: chr  "No" "No" "Yes" "No" ...
##  $ Q116441: chr  "No" "Yes" "No" "No" ...
##  $ Q116197: chr  "P.M." "A.M." "A.M." "A.M." ...
##  $ Q115611: chr  "No" "No" "Yes" "No" ...
##  $ Q115899: chr  "Circumstances" "Me" "Circumstances" "Circumstances" ...
##  $ Q115390: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q114748: chr  "Yes" "No" "No" "No" ...
##  $ Q115195: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q113584: chr  "Technology" "" "Technology" "People" ...
##  $ Q112478: chr  "No" "" "Yes" "Yes" ...
##  $ Q112270: chr  "" "" "Yes" "Yes" ...
##  $ Q111848: chr  "No" "" "No" "Yes" ...
##  $ Q106993: chr  "Yes" "No" "Yes" "Yes" ...
##  $ Q106388: chr  "No" "Yes" "No" "No" ...
##  $ Q105655: chr  "No" "No" "No" "Yes" ...
##  $ Q104996: chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q102674: chr  "No" "" "No" "No" ...
## NULL
## 'data.frame':    5568 obs. of  21 variables:
##  $ Q102674: chr  "No" "" "No" "No" ...
##  $ Q102687: chr  "Yes" "" "Yes" "Yes" ...
##  $ Q102289: chr  "No" "" "No" "Yes" ...
##  $ Q102089: chr  "Own" "" "Own" "Own" ...
##  $ Q101162: chr  "Optimist" "" "Pessimist" "Optimist" ...
##  $ Q101163: chr  "" "" "Mom" "Mom" ...
##  $ Q101596: chr  "Yes" "" "No" "No" ...
##  $ Q100689: chr  "Yes" "" "No" "No" ...
##  $ Q100680: chr  "No" "" "No" "No" ...
##  $ Q100562: chr  "No" "" "No" "Yes" ...
##  $ Q99982 : chr  "Nope" "" "Nope" "Check!" ...
##  $ Q100010: chr  "Yes" "" "Yes" "No" ...
##  $ Q99716 : chr  "No" "" "No" "No" ...
##  $ Q99581 : chr  "No" "" "No" "No" ...
##  $ Q99480 : chr  "" "No" "No" "Yes" ...
##  $ Q98869 : chr  "No" "No" "Yes" "Yes" ...
##  $ Q98578 : chr  "" "No" "No" "No" ...
##  $ Q98059 : chr  "Only-child" "Only-child" "Yes" "Yes" ...
##  $ Q98078 : chr  "No" "Yes" "No" "No" ...
##  $ Q98197 : chr  "No" "No" "Yes" "No" ...
##  $ Q96024 : chr  "Yes" "No" "No" "Yes" ...
## NULL
## Warning in myprint_str_df(obsDf): [list output truncated]
## [1] "Reading file ./data/test2016.csv..."
## [1] "dimensions of data in ./data/test2016.csv: 1,392 rows x 107 cols"
##   USER_ID  YOB Gender             Income   HouseholdStatus
## 1       2 1985 Female  $25,001 - $50,000  Single (no kids)
## 2       3 1983   Male  $50,000 - $74,999  Married (w/kids)
## 3       6 1995   Male $75,000 - $100,000  Single (no kids)
## 4       7 1980 Female  $50,000 - $74,999  Single (no kids)
## 5      14 1980 Female                    Married (no kids)
## 6      28 1973   Male      over $150,000 Married (no kids)
##          EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1       Master's Degree             Yes      No     Yes      No      No
## 2 Current Undergraduate                      No             Yes     Yes
## 3          Current K-12                                                
## 4       Master's Degree     Yes     Yes      No     Yes     Yes     Yes
## 5 Current Undergraduate             Yes      No     Yes      No      No
## 6       Master's Degree      No     Yes      No     Yes      No      No
##   Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650 Q120472
## 1  Public      No     Yes     Yes     Yes      No     Yes     Yes Science
## 2  Public      No     Yes      No                                        
## 3                      No      No      No     Yes      No     Yes Science
## 4  Public      No     Yes      No     Yes      No     Yes     Yes Science
## 5  Public     Yes     Yes      No     Yes     Yes      No     Yes     Art
## 6  Public      No     Yes      No     Yes     Yes     Yes     Yes Science
##       Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892 Q118117
## 1 Study first     Yes     Yes     Yes      No  Giving     Yes      No
## 2 Study first      No     Yes              No                        
## 3   Try first      No     Yes      No     Yes  Giving                
## 4   Try first     Yes      No      No     Yes  Giving     Yes     Yes
## 5   Try first     Yes     Yes     Yes     Yes  Giving      No      No
## 6   Try first     Yes     Yes      No      No  Giving      No     Yes
##      Q118232 Q118233 Q118237     Q117186        Q117193 Q116797 Q116881
## 1   Idealist      No     Yes Cool headed      Odd hours     Yes   Happy
## 2                                                                      
## 3                                                                      
## 4   Idealist      No      No Cool headed Standard hours      No   Happy
## 5   Idealist      No     Yes  Hot headed Standard hours     Yes   Happy
## 6 Pragmatist     Yes      No  Hot headed      Odd hours     Yes   Right
##   Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610 Q115611
## 1     Yes     Yes      No     Yes    A.M.     Yes     End     Yes      No
## 2     Yes     Yes                    P.M.                                
## 3     Yes                                                                
## 4     Yes      No      No     Yes    A.M.     Yes   Start     Yes      No
## 5     Yes     Yes     Yes      No    P.M.     Yes     End      No      No
## 6     Yes     Yes     Yes     Yes    P.M.             End     Yes     Yes
##         Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386 Q113992
## 1            Me      No     Yes      No     Yes     Yes     TMI        
## 2                                            No                     Yes
## 3                   Yes      No     Yes     Yes      No     TMI      No
## 4            Me     Yes      No     Yes     Yes     Yes     TMI      No
## 5            Me      No      No      No     Yes      No     TMI      No
## 6 Circumstances      No     Yes      No     Yes      No     TMI     Yes
##   Q114152 Q113583    Q113584 Q113181 Q112478 Q112512 Q112270 Q111848
## 1      No   Tunes     People     Yes     Yes      No     Yes     Yes
## 2      No                         No                      No     Yes
## 3      No   Tunes Technology     Yes      No     Yes      No        
## 4     Yes    Talk     People      No      No     Yes      No     Yes
## 5           Tunes Technology      No     Yes     Yes             Yes
## 6      No    Talk Technology      No     Yes     Yes      No     Yes
##      Q111580 Q111220 Q110740 Q109367  Q108950 Q109244 Q108855 Q108617
## 1 Supportive      No             Yes Cautious     Yes    Yes!        
## 2                 No             Yes Cautious      No    Yes!      No
## 3                                 No               No              No
## 4 Supportive      No      PC      No Cautious     Yes    Yes!      No
## 5 Supportive     Yes     Mac     Yes Cautious      No    Yes!      No
## 6  Demanding      No      PC     Yes Cautious      No  Umm...      No
##   Q108856 Q108754   Q108342 Q108343 Q107869 Q107491 Q106993     Q106997
## 1             Yes In-person     Yes                                    
## 2   Space      No                       Yes     Yes     Yes Grrr people
## 3             Yes In-person      No      No     Yes     Yes Yay people!
## 4   Space      No    Online      No      No     Yes     Yes Yay people!
## 5   Space      No In-person      No      No     Yes      No Grrr people
## 6   Space      No In-person     Yes             Yes     Yes Grrr people
##   Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996 Q103293 Q102906
## 1                                                                        
## 2     Yes      No      No     Yes      No     Yes      No      No        
## 3     Yes      No     Yes      No      No     Yes     Yes      No      No
## 4      No      No      No      No      No     Yes     Yes      No      No
## 5      No      No      No     Yes     Yes     Yes     Yes     Yes      No
## 6     Yes      No     Yes     Yes      No      No      No     Yes     Yes
##   Q102674 Q102687 Q102289 Q102089   Q101162 Q101163 Q101596 Q100689
## 1                                                                No
## 2                            Rent Pessimist     Dad                
## 3      No      No     Yes     Own  Optimist     Mom      No      No
## 4      No      No      No     Own  Optimist     Dad      No      No
## 5     Yes      No      No     Own Pessimist     Mom      No     Yes
## 6     Yes     Yes      No     Own Pessimist     Mom      No     Yes
##   Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480 Q98869 Q98578 Q98059
## 1     Yes     Yes                                        Yes              
## 2             Yes                                        Yes           Yes
## 3     Yes     Yes   Nope      No     No     No    Yes    Yes     No    Yes
## 4     Yes     Yes   Nope     Yes     No     No     No    Yes     No    Yes
## 5     Yes     Yes   Nope     Yes     No     No    Yes     No     No    Yes
## 6     Yes     Yes   Nope     Yes     No     No    Yes     No     No    Yes
##   Q98078 Q98197 Q96024
## 1                     
## 2    Yes     No    Yes
## 3     No    Yes    Yes
## 4     No     No    Yes
## 5     No     No     No
## 6     No     No    Yes
##      USER_ID  YOB Gender              Income   HouseholdStatus
## 503     2555 1956   Male       over $150,000  Married (w/kids)
## 515     2616 1959   Male       over $150,000  Married (w/kids)
## 857     4346 1990 Female   $50,000 - $74,999                  
## 950     4814 1969   Male  $75,000 - $100,000  Married (w/kids)
## 1207    6057 1937 Female   $25,001 - $50,000 Married (no kids)
## 1255    6285 1976 Female $100,001 - $150,000 Married (no kids)
##         EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 503  Bachelor's Degree      No      No      No     Yes      No     Yes
## 515  Bachelor's Degree                                                
## 857  Bachelor's Degree                                                
## 950  Bachelor's Degree             Yes      No     Yes      No      No
## 1207 Bachelor's Degree                                      No     Yes
## 1255 Bachelor's Degree                                                
##      Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 503  Private      No     Yes      No      No     Yes      No     Yes
## 515               No      No                                        
## 857               No     Yes      No      No      No      No     Yes
## 950   Public     Yes     Yes      No     Yes     Yes      No     Yes
## 1207  Public      No     Yes      No      No      No              No
## 1255                                                                
##      Q120472     Q120194 Q120012 Q120014 Q119334 Q119851   Q119650 Q118892
## 503  Science Study first      No     Yes      No     Yes    Giving     Yes
## 515                                                                    Yes
## 857  Science Study first      No      No     Yes      No Receiving     Yes
## 950  Science Study first      No      No      No      No    Giving      No
## 1207         Study first      No      No             Yes Receiving     Yes
## 1255                                                                      
##      Q118117    Q118232 Q118233 Q118237     Q117186        Q117193 Q116797
## 503       No Pragmatist      No      No Cool headed Standard hours      No
## 515       No Pragmatist      No     Yes Cool headed Standard hours      No
## 857      Yes Pragmatist      No      No Cool headed      Odd hours      No
## 950       No Pragmatist      No     Yes  Hot headed      Odd hours     Yes
## 1207      No Pragmatist      No      No  Hot headed                     No
## 1255                                                                      
##      Q116881 Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777
## 503    Happy     Yes     Yes      No      No    A.M.     Yes     End
## 515    Right     Yes     Yes      No     Yes             Yes        
## 857    Right     Yes     Yes      No      No    A.M.     Yes   Start
## 950    Happy     Yes     Yes     Yes      No    P.M.     Yes   Start
## 1207   Happy     Yes     Yes      No      No    A.M.     Yes   Start
## 1255                     Yes      No     Yes    A.M.     Yes   Start
##      Q115610 Q115611       Q115899 Q115390 Q114961 Q114748 Q115195 Q114517
## 503      Yes     Yes            Me      No      No      No     Yes     Yes
## 515      Yes      No            Me     Yes      No     Yes     Yes      No
## 857      Yes      No            Me              No      No      No     Yes
## 950      Yes      No            Me     Yes      No     Yes      No      No
## 1207      No      No Circumstances     Yes      No     Yes      No     Yes
## 1255     Yes      No Circumstances      No     Yes      No     Yes     Yes
##         Q114386 Q113992 Q114152 Q113583    Q113584 Q113181 Q112478 Q112512
## 503         TMI     Yes     Yes   Tunes     People     Yes      No     Yes
## 515                  No     Yes    Talk Technology                        
## 857  Mysterious      No      No   Tunes     People      No      No      No
## 950  Mysterious      No      No   Tunes     People     Yes     Yes     Yes
## 1207                Yes      No    Talk                                Yes
## 1255        TMI             Yes                                Yes     Yes
##      Q112270 Q111848    Q111580 Q111220 Q110740 Q109367       Q108950
## 503       No     Yes  Demanding      No      PC      No      Cautious
## 515       No     Yes                 No     Mac     Yes              
## 857      Yes     Yes Supportive      No     Mac      No Risk-friendly
## 950       No     Yes Supportive     Yes      PC      No      Cautious
## 1207                 Supportive      No      PC              Cautious
## 1255     Yes     Yes  Demanding      No     Mac                      
##      Q109244 Q108855 Q108617 Q108856 Q108754   Q108342 Q108343 Q107869
## 503       No  Umm...      No   Space      No In-person      No     Yes
## 515                                                                   
## 857      Yes  Umm...      No   Space      No In-person      No     Yes
## 950       No    Yes!      No   Space      No In-person      No      No
## 1207            Yes!      No   Space      No In-person      No     Yes
## 1255                                                                  
##      Q107491 Q106993     Q106997 Q106272 Q106388 Q106389 Q106042 Q105840
## 503      Yes     Yes Yay people!     Yes      No      No     Yes      No
## 515                                                                   No
## 857       No     Yes Grrr people     Yes      No     Yes      No      No
## 950      Yes      No Grrr people     Yes     Yes      No      No      No
## 1207     Yes     Yes                 Yes                                
## 1255                                                                    
##      Q105655 Q104996 Q103293 Q102906 Q102674 Q102687 Q102289 Q102089
## 503       No     Yes      No      No      No     Yes      No     Own
## 515      Yes     Yes                                                
## 857       No     Yes     Yes      No      No     Yes     Yes     Own
## 950      Yes     Yes     Yes      No      No     Yes      No     Own
## 1207     Yes                                                        
## 1255                                                                
##        Q101162 Q101163 Q101596 Q100689 Q100680 Q100562 Q99982 Q100010
## 503  Pessimist     Mom     Yes     Yes      No     Yes Check!     Yes
## 515                                                    Check!     Yes
## 857   Optimist     Mom      No     Yes     Yes      No   Nope     Yes
## 950  Pessimist     Mom     Yes      No      No      No Check!     Yes
## 1207                                                                 
## 1255                                                                 
##      Q99716 Q99581 Q99480 Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 503      No     No    Yes    Yes     No    Yes    Yes    Yes    Yes
## 515             No    Yes    Yes           Yes     No    Yes    Yes
## 857      No    Yes    Yes    Yes     No    Yes     No     No     No
## 950      No     No    Yes    Yes     No    Yes     No    Yes    Yes
## 1207                                                               
## 1255                                                               
##      USER_ID  YOB Gender              Income             HouseholdStatus
## 1387    6922 1988   Male   $50,000 - $74,999            Single (no kids)
## 1388    6928 1977 Female   $50,000 - $74,999 Domestic Partners (no kids)
## 1389    6930 1998 Female $100,001 - $150,000            Single (no kids)
## 1390    6941 1989   Male   $25,001 - $50,000           Married (no kids)
## 1391    6946 1996   Male                                                
## 1392    6947   NA Female                                                
##         EducationLevel Q124742 Q124122 Q123464 Q123621 Q122769 Q122770
## 1387   Master's Degree                                                
## 1388   Master's Degree                                                
## 1389      Current K-12                                      No      No
## 1390 Bachelor's Degree                                                
## 1391      Current K-12                                                
## 1392                       Yes     Yes      No      No      No      No
##      Q122771 Q122120 Q121699 Q121700 Q120978 Q121011 Q120379 Q120650
## 1387                     Yes     Yes     Yes     Yes     Yes     Yes
## 1388                             Yes              No             Yes
## 1389  Public     Yes     Yes     Yes     Yes     Yes     Yes     Yes
## 1390             Yes     Yes      No      No      No                
## 1391             Yes      No      No     Yes      No     Yes     Yes
## 1392  Public     Yes     Yes      No     Yes     Yes     Yes     Yes
##      Q120472     Q120194 Q120012 Q120014 Q119334 Q119851 Q119650 Q118892
## 1387 Science   Try first      No     Yes     Yes      No  Giving        
## 1388     Art                                                            
## 1389     Art Study first     Yes      No     Yes      No  Giving        
## 1390                                                                    
## 1391     Art Study first     Yes     Yes     Yes      No  Giving        
## 1392     Art                  No      No      No     Yes  Giving        
##      Q118117 Q118232 Q118233 Q118237 Q117186 Q117193 Q116797 Q116881
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q116953 Q116601 Q116441 Q116448 Q116197 Q115602 Q115777 Q115610
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q115611 Q115899 Q115390 Q114961 Q114748 Q115195 Q114517 Q114386
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q113992 Q114152 Q113583 Q113584 Q113181 Q112478 Q112512 Q112270
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q111848 Q111580 Q111220 Q110740 Q109367 Q108950 Q109244 Q108855
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q108617 Q108856 Q108754 Q108342 Q108343 Q107869 Q107491 Q106993
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q106997 Q106272 Q106388 Q106389 Q106042 Q105840 Q105655 Q104996
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q103293 Q102906 Q102674 Q102687 Q102289 Q102089 Q101162 Q101163
## 1387                                                                
## 1388                                                                
## 1389                                                                
## 1390                                                                
## 1391                                                                
## 1392                                                                
##      Q101596 Q100689 Q100680 Q100562 Q99982 Q100010 Q99716 Q99581 Q99480
## 1387                                                                    
## 1388                                                                    
## 1389                                                                    
## 1390                                                                    
## 1391                                                                    
## 1392                                                                    
##      Q98869 Q98578 Q98059 Q98078 Q98197 Q96024
## 1387                                          
## 1388                                          
## 1389                                          
## 1390                                          
## 1391                                          
## 1392                                          
## 'data.frame':    1392 obs. of  20 variables:
##  $ USER_ID        : int  2 3 6 7 14 28 29 37 44 56 ...
##  $ YOB            : int  1985 1983 1995 1980 1980 1973 1968 1961 1989 1975 ...
##  $ Gender         : chr  "Female" "Male" "Male" "Female" ...
##  $ Income         : chr  "$25,001 - $50,000" "$50,000 - $74,999" "$75,000 - $100,000" "$50,000 - $74,999" ...
##  $ HouseholdStatus: chr  "Single (no kids)" "Married (w/kids)" "Single (no kids)" "Single (no kids)" ...
##  $ EducationLevel : chr  "Master's Degree" "Current Undergraduate" "Current K-12" "Master's Degree" ...
##  $ Q124742        : chr  "" "" "" "Yes" ...
##  $ Q124122        : chr  "Yes" "" "" "Yes" ...
##  $ Q123464        : chr  "No" "No" "" "No" ...
##  $ Q123621        : chr  "Yes" "" "" "Yes" ...
##  $ Q122769        : chr  "No" "Yes" "" "Yes" ...
##  $ Q122770        : chr  "No" "Yes" "" "Yes" ...
##  $ Q122771        : chr  "Public" "Public" "" "Public" ...
##  $ Q122120        : chr  "No" "No" "" "No" ...
##  $ Q121699        : chr  "Yes" "Yes" "No" "Yes" ...
##  $ Q121700        : chr  "Yes" "No" "No" "No" ...
##  $ Q120978        : chr  "Yes" "" "No" "Yes" ...
##  $ Q121011        : chr  "No" "" "Yes" "No" ...
##  $ Q120379        : chr  "Yes" "" "No" "Yes" ...
##  $ Q120650        : chr  "Yes" "" "Yes" "Yes" ...
## NULL
## 'data.frame':    1392 obs. of  20 variables:
##  $ Q120012: chr  "Yes" "No" "No" "Yes" ...
##  $ Q120014: chr  "Yes" "Yes" "Yes" "No" ...
##  $ Q118117: chr  "No" "" "" "Yes" ...
##  $ Q118237: chr  "Yes" "" "" "No" ...
##  $ Q116953: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q116601: chr  "Yes" "Yes" "" "No" ...
##  $ Q116448: chr  "Yes" "" "" "Yes" ...
##  $ Q116197: chr  "A.M." "P.M." "" "A.M." ...
##  $ Q115899: chr  "Me" "" "" "Me" ...
##  $ Q114961: chr  "Yes" "" "No" "No" ...
##  $ Q113584: chr  "People" "" "Technology" "People" ...
##  $ Q113181: chr  "Yes" "No" "Yes" "No" ...
##  $ Q112512: chr  "No" "" "Yes" "Yes" ...
##  $ Q108950: chr  "Cautious" "Cautious" "" "Cautious" ...
##  $ Q108617: chr  "" "No" "No" "No" ...
##  $ Q108342: chr  "In-person" "" "In-person" "Online" ...
##  $ Q107491: chr  "" "Yes" "Yes" "Yes" ...
##  $ Q106272: chr  "" "Yes" "Yes" "No" ...
##  $ Q106389: chr  "" "No" "Yes" "No" ...
##  $ Q104996: chr  "" "No" "Yes" "Yes" ...
## NULL
## 'data.frame':    1392 obs. of  21 variables:
##  $ Q102674: chr  "" "" "No" "No" ...
##  $ Q102687: chr  "" "" "No" "No" ...
##  $ Q102289: chr  "" "" "Yes" "No" ...
##  $ Q102089: chr  "" "Rent" "Own" "Own" ...
##  $ Q101162: chr  "" "Pessimist" "Optimist" "Optimist" ...
##  $ Q101163: chr  "" "Dad" "Mom" "Dad" ...
##  $ Q101596: chr  "" "" "No" "No" ...
##  $ Q100689: chr  "No" "" "No" "No" ...
##  $ Q100680: chr  "Yes" "" "Yes" "Yes" ...
##  $ Q100562: chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q99982 : chr  "" "" "Nope" "Nope" ...
##  $ Q100010: chr  "" "" "No" "Yes" ...
##  $ Q99716 : chr  "" "" "No" "No" ...
##  $ Q99581 : chr  "" "" "No" "No" ...
##  $ Q99480 : chr  "" "" "Yes" "No" ...
##  $ Q98869 : chr  "Yes" "Yes" "Yes" "Yes" ...
##  $ Q98578 : chr  "" "" "No" "No" ...
##  $ Q98059 : chr  "" "Yes" "Yes" "Yes" ...
##  $ Q98078 : chr  "" "Yes" "No" "No" ...
##  $ Q98197 : chr  "" "No" "Yes" "No" ...
##  $ Q96024 : chr  "" "Yes" "Yes" "Yes" ...
## NULL
## Warning in myprint_str_df(obsDf): [list output truncated]
## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: YOB.Age.fctr..."
## [1] "Creating new feature: YOB.Age.dff..."
## [1] "Creating new feature: Gender.fctr..."
## [1] "Creating new feature: Income.fctr..."
## [1] "Creating new feature: Hhold.fctr..."
## [1] "Creating new feature: Edn.fctr..."
## [1] "Creating new feature: Q124742.fctr..."
## [1] "Creating new feature: Q124122.fctr..."
## [1] "Creating new feature: Q123621.fctr..."
## [1] "Creating new feature: Q123464.fctr..."
## [1] "Creating new feature: Q122771.fctr..."
## [1] "Creating new feature: Q122770.fctr..."
## [1] "Creating new feature: Q122769.fctr..."
## [1] "Creating new feature: Q122120.fctr..."
## [1] "Creating new feature: Q121700.fctr..."
## [1] "Creating new feature: Q121699.fctr..."
## [1] "Creating new feature: Q121011.fctr..."
## [1] "Creating new feature: Q120978.fctr..."
## [1] "Creating new feature: Q120650.fctr..."
## [1] "Creating new feature: Q120472.fctr..."
## [1] "Creating new feature: Q120379.fctr..."
## [1] "Creating new feature: Q120194.fctr..."
## [1] "Creating new feature: Q120014.fctr..."
## [1] "Creating new feature: Q120012.fctr..."
## [1] "Creating new feature: Q119851.fctr..."
## [1] "Creating new feature: Q119650.fctr..."
## [1] "Creating new feature: Q119334.fctr..."
## [1] "Creating new feature: Q118892.fctr..."
## [1] "Creating new feature: Q118237.fctr..."
## [1] "Creating new feature: Q118233.fctr..."
## [1] "Creating new feature: Q118232.fctr..."
## [1] "Creating new feature: Q118117.fctr..."
## [1] "Creating new feature: Q117193.fctr..."
## [1] "Creating new feature: Q117186.fctr..."
## [1] "Creating new feature: Q116797.fctr..."
## [1] "Creating new feature: Q116881.fctr..."
## [1] "Creating new feature: Q116953.fctr..."
## [1] "Creating new feature: Q116601.fctr..."
## [1] "Creating new feature: Q116441.fctr..."
## [1] "Creating new feature: Q116448.fctr..."
## [1] "Creating new feature: Q116197.fctr..."
## [1] "Creating new feature: Q115602.fctr..."
## [1] "Creating new feature: Q115777.fctr..."
## [1] "Creating new feature: Q115610.fctr..."
## [1] "Creating new feature: Q115611.fctr..."
## [1] "Creating new feature: Q115899.fctr..."
## [1] "Creating new feature: Q115390.fctr..."
## [1] "Creating new feature: Q115195.fctr..."
## [1] "Creating new feature: Q114961.fctr..."
## [1] "Creating new feature: Q114748.fctr..."
## [1] "Creating new feature: Q114517.fctr..."
## [1] "Creating new feature: Q114386.fctr..."
## [1] "Creating new feature: Q114152.fctr..."
## [1] "Creating new feature: Q113992.fctr..."
## [1] "Creating new feature: Q113583.fctr..."
## [1] "Creating new feature: Q113584.fctr..."
## [1] "Creating new feature: Q113181.fctr..."
## [1] "Creating new feature: Q112478.fctr..."
## [1] "Creating new feature: Q112512.fctr..."
## [1] "Creating new feature: Q112270.fctr..."
## [1] "Creating new feature: Q111848.fctr..."
## [1] "Creating new feature: Q111580.fctr..."
## [1] "Creating new feature: Q111220.fctr..."
## [1] "Creating new feature: Q110740.fctr..."
## [1] "Creating new feature: Q109367.fctr..."
## [1] "Creating new feature: Q109244.fctr..."
## [1] "Creating new feature: Q108950.fctr..."
## [1] "Creating new feature: Q108855.fctr..."
## [1] "Creating new feature: Q108617.fctr..."
## [1] "Creating new feature: Q108856.fctr..."
## [1] "Creating new feature: Q108754.fctr..."
## [1] "Creating new feature: Q108342.fctr..."
## [1] "Creating new feature: Q108343.fctr..."
## [1] "Creating new feature: Q107869.fctr..."
## [1] "Creating new feature: Q107491.fctr..."
## [1] "Creating new feature: Q106993.fctr..."
## [1] "Creating new feature: Q106997.fctr..."
## [1] "Creating new feature: Q106272.fctr..."
## [1] "Creating new feature: Q106388.fctr..."
## [1] "Creating new feature: Q106389.fctr..."
## [1] "Creating new feature: Q106042.fctr..."
## [1] "Creating new feature: Q105840.fctr..."
## [1] "Creating new feature: Q105655.fctr..."
## [1] "Creating new feature: Q104996.fctr..."
## [1] "Creating new feature: Q103293.fctr..."
## [1] "Creating new feature: Q102906.fctr..."
## [1] "Creating new feature: Q102674.fctr..."
## [1] "Creating new feature: Q102687.fctr..."
## [1] "Creating new feature: Q102289.fctr..."
## [1] "Creating new feature: Q102089.fctr..."
## [1] "Creating new feature: Q101162.fctr..."
## [1] "Creating new feature: Q101163.fctr..."
## [1] "Creating new feature: Q101596.fctr..."
## [1] "Creating new feature: Q100689.fctr..."
## [1] "Creating new feature: Q100680.fctr..."
## [1] "Creating new feature: Q100562.fctr..."
## [1] "Creating new feature: Q100010.fctr..."
## [1] "Creating new feature: Q99982.fctr..."
## [1] "Creating new feature: Q99716.fctr..."
## [1] "Creating new feature: Q99581.fctr..."
## [1] "Creating new feature: Q99480.fctr..."
## [1] "Creating new feature: Q98869.fctr..."
## [1] "Creating new feature: Q98578.fctr..."
## [1] "Creating new feature: Q98197.fctr..."
## [1] "Creating new feature: Q98059.fctr..."
## [1] "Creating new feature: Q98078.fctr..."
## [1] "Creating new feature: Q96024.fctr..."
## [1] "Partition stats:"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
##        Party  .src   .n
## 1   Democrat Train 2951
## 2 Republican Train 2617
## 3       <NA>  Test 1392
##        Party  .src   .n
## 1   Democrat Train 2951
## 2 Republican Train 2617
## 3       <NA>  Test 1392
## Loading required package: RColorBrewer

##    .src   .n
## 1 Train 5568
## 2  Test 1392
## [1] "Running glbObsDropCondition filter: (is.na(glbObsAll[, \"Q109244\"]) | (glbObsAll[, \"Q109244\"] != \"No\"))"
## [1] "Partition stats:"
##        Party  .src   .n
## 1 Republican Train 1421
## 2   Democrat Train 1038
## 3       <NA>  Test  622
##        Party  .src   .n
## 1 Republican Train 1421
## 2   Democrat Train 1038
## 3       <NA>  Test  622

##    .src   .n
## 1 Train 2459
## 2  Test  622
## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
## [1] "Found 0 duplicates by all features:"
## NULL
##          label step_major step_minor label_minor    bgn    end elapsed
## 1  import.data          1          0           0  6.566 13.729   7.163
## 2 inspect.data          2          0           0 13.729     NA      NA

Step 2.0: inspect data

## Warning: Removed 622 rows containing non-finite values (stat_count).
## Loading required package: reshape2

##       Party.Democrat Party.Republican Party.NA
## Test              NA               NA      622
## Train           1038             1421       NA
##       Party.Democrat Party.Republican Party.NA
## Test              NA               NA        1
## Train      0.4221228        0.5778772       NA
## [1] "numeric data missing in : "
## YOB 
## 128 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
##        Party Party.fctr   .n
## 1 Republican          R 1421
## 2   Democrat          D 1038
## 3       <NA>       <NA>  622
## Warning: Removed 1 rows containing missing values (position_stack).

##       Party.fctr.D Party.fctr.R Party.fctr.NA
## Test            NA           NA           622
## Train         1038         1421            NA
##       Party.fctr.D Party.fctr.R Party.fctr.NA
## Test            NA           NA             1
## Train    0.4221228    0.5778772            NA

## [1] "elapsed Time (secs): 4.219000"
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "elapsed Time (secs): 140.434000"
## [1] "elapsed Time (secs): 140.434000"
##          label step_major step_minor label_minor     bgn     end elapsed
## 2 inspect.data          2          0           0  13.729 160.469  146.74
## 3   scrub.data          2          1           1 160.470      NA      NA

Step 2.1: scrub data

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
##            label step_major step_minor label_minor     bgn     end elapsed
## 3     scrub.data          2          1           1 160.470 197.292  36.822
## 4 transform.data          2          2           2 197.293      NA      NA

Step 2.2: transform data

##              label step_major step_minor label_minor     bgn     end
## 4   transform.data          2          2           2 197.293 197.335
## 5 extract.features          3          0           0 197.335      NA
##   elapsed
## 4   0.042
## 5      NA

Step 3.0: extract features

##                       label step_major step_minor label_minor     bgn
## 5          extract.features          3          0           0 197.335
## 6 extract.features.datetime          3          1           1 197.356
##       end elapsed
## 5 197.356   0.021
## 6      NA      NA

Step 3.1: extract features datetime

##                           label step_major step_minor label_minor     bgn
## 1 extract.features.datetime.bgn          1          0           0 197.384
##   end elapsed
## 1  NA      NA
##                       label step_major step_minor label_minor     bgn
## 6 extract.features.datetime          3          1           1 197.356
## 7    extract.features.image          3          2           2 197.396
##       end elapsed
## 6 197.396    0.04
## 7      NA      NA

Step 3.2: extract features image

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.image.bgn          1          0           0 197.429  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 197.429
## 2 extract.features.image.end          2          0           0 197.439
##       end elapsed
## 1 197.439    0.01
## 2      NA      NA
##                        label step_major step_minor label_minor     bgn
## 1 extract.features.image.bgn          1          0           0 197.429
## 2 extract.features.image.end          2          0           0 197.439
##       end elapsed
## 1 197.439    0.01
## 2      NA      NA
##                    label step_major step_minor label_minor     bgn     end
## 7 extract.features.image          3          2           2 197.396 197.449
## 8 extract.features.price          3          3           3 197.450      NA
##   elapsed
## 7   0.053
## 8      NA

Step 3.3: extract features price

##                        label step_major step_minor label_minor     bgn end
## 1 extract.features.price.bgn          1          0           0 197.476  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor     bgn     end
## 8 extract.features.price          3          3           3 197.450 197.485
## 9  extract.features.text          3          4           4 197.486      NA
##   elapsed
## 8   0.035
## 9      NA

Step 3.4: extract features text

##                       label step_major step_minor label_minor     bgn end
## 1 extract.features.text.bgn          1          0           0 197.529  NA
##   elapsed
## 1      NA
## Warning in rm(tmp_allobs_df): object 'tmp_allobs_df' not found
## Warning in rm(tmp_trnobs_df): object 'tmp_trnobs_df' not found
##                      label step_major step_minor label_minor     bgn
## 9    extract.features.text          3          4           4 197.486
## 10 extract.features.string          3          5           5 197.543
##        end elapsed
## 9  197.542   0.056
## 10      NA      NA

Step 3.5: extract features string

##                         label step_major step_minor label_minor     bgn
## 1 extract.features.string.bgn          1          0           0 197.578
##   end elapsed
## 1  NA      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor     bgn     end elapsed
## 1           0 197.578 197.587   0.009
## 2           0 197.588      NA      NA
##            Gender            Income   HouseholdStatus    EducationLevel 
##          "Gender"          "Income" "HouseholdStatus"  "EducationLevel" 
##             Party           Q124742           Q124122           Q123464 
##           "Party"         "Q124742"         "Q124122"         "Q123464" 
##           Q123621           Q122769           Q122770           Q122771 
##         "Q123621"         "Q122769"         "Q122770"         "Q122771" 
##           Q122120           Q121699           Q121700           Q120978 
##         "Q122120"         "Q121699"         "Q121700"         "Q120978" 
##           Q121011           Q120379           Q120650           Q120472 
##         "Q121011"         "Q120379"         "Q120650"         "Q120472" 
##           Q120194           Q120012           Q120014           Q119334 
##         "Q120194"         "Q120012"         "Q120014"         "Q119334" 
##           Q119851           Q119650           Q118892           Q118117 
##         "Q119851"         "Q119650"         "Q118892"         "Q118117" 
##           Q118232           Q118233           Q118237           Q117186 
##         "Q118232"         "Q118233"         "Q118237"         "Q117186" 
##           Q117193           Q116797           Q116881           Q116953 
##         "Q117193"         "Q116797"         "Q116881"         "Q116953" 
##           Q116601           Q116441           Q116448           Q116197 
##         "Q116601"         "Q116441"         "Q116448"         "Q116197" 
##           Q115602           Q115777           Q115610           Q115611 
##         "Q115602"         "Q115777"         "Q115610"         "Q115611" 
##           Q115899           Q115390           Q114961           Q114748 
##         "Q115899"         "Q115390"         "Q114961"         "Q114748" 
##           Q115195           Q114517           Q114386           Q113992 
##         "Q115195"         "Q114517"         "Q114386"         "Q113992" 
##           Q114152           Q113583           Q113584           Q113181 
##         "Q114152"         "Q113583"         "Q113584"         "Q113181" 
##           Q112478           Q112512           Q112270           Q111848 
##         "Q112478"         "Q112512"         "Q112270"         "Q111848" 
##           Q111580           Q111220           Q110740           Q109367 
##         "Q111580"         "Q111220"         "Q110740"         "Q109367" 
##           Q108950           Q109244           Q108855           Q108617 
##         "Q108950"         "Q109244"         "Q108855"         "Q108617" 
##           Q108856           Q108754           Q108342           Q108343 
##         "Q108856"         "Q108754"         "Q108342"         "Q108343" 
##           Q107869           Q107491           Q106993           Q106997 
##         "Q107869"         "Q107491"         "Q106993"         "Q106997" 
##           Q106272           Q106388           Q106389           Q106042 
##         "Q106272"         "Q106388"         "Q106389"         "Q106042" 
##           Q105840           Q105655           Q104996           Q103293 
##         "Q105840"         "Q105655"         "Q104996"         "Q103293" 
##           Q102906           Q102674           Q102687           Q102289 
##         "Q102906"         "Q102674"         "Q102687"         "Q102289" 
##           Q102089           Q101162           Q101163           Q101596 
##         "Q102089"         "Q101162"         "Q101163"         "Q101596" 
##           Q100689           Q100680           Q100562            Q99982 
##         "Q100689"         "Q100680"         "Q100562"          "Q99982" 
##           Q100010            Q99716            Q99581            Q99480 
##         "Q100010"          "Q99716"          "Q99581"          "Q99480" 
##            Q98869            Q98578            Q98059            Q98078 
##          "Q98869"          "Q98578"          "Q98059"          "Q98078" 
##            Q98197            Q96024              .src 
##          "Q98197"          "Q96024"            ".src"
##                      label step_major step_minor label_minor     bgn
## 10 extract.features.string          3          5           5 197.543
## 11    extract.features.end          3          6           6 197.610
##        end elapsed
## 10 197.609   0.066
## 11      NA      NA

Step 3.6: extract features end

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

##                   label step_major step_minor label_minor     bgn     end
## 11 extract.features.end          3          6           6 197.610 198.543
## 12  manage.missing.data          4          0           0 198.544      NA
##    elapsed
## 11   0.933
## 12      NA

Step 4.0: manage missing data

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
##                  label step_major step_minor label_minor     bgn     end
## 12 manage.missing.data          4          0           0 198.544 199.193
## 13        cluster.data          5          0           0 199.194      NA
##    elapsed
## 12    0.65
## 13      NA

Step 5.0: cluster data

```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## Loading required package: proxy
## 
## Attaching package: 'proxy'
## The following objects are masked from 'package:stats':
## 
##     as.dist, dist
## The following object is masked from 'package:base':
## 
##     as.matrix
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr
## Loading required package: ggdendro
## [1] "Clustering features: "
## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero
##               abs.cor.y
## Q108855.fctr 0.05525571
## Q116881.fctr 0.05625959
## Q98197.fctr  0.07400689
## Q113181.fctr 0.09842608
## Q115611.fctr 0.10612270
## [1] "    .rnorm cor: -0.0049"
## [1] "  Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6810"
##   Q109244.fctr .clusterid Q109244.fctr.clusterid    D    R  .entropy .knt
## 1           No          1                   No_1 1038 1421 0.6809679 2459
## [1] "glbObsAll$Q109244.fctr Entropy: 0.6810 (100.0000 pct)"
## [1] "Category: No"
## [1] "max distance(0.9770) pair:"
##      USER_ID Party.fctr Q109244.fctr Q124742.fctr Q124122.fctr
## 2961    3680          R           No           NA           NA
## 3852    4799          R           No           NA           NA
##      Q123621.fctr Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr
## 2961          Yes           No           Pt          Yes           No
## 3852           NA           NA           NA           NA           NA
##      Q122120.fctr Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr
## 2961           No           No          Yes          Yes          Yes
## 3852           NA           NA           NA           NA           NA
##      Q120650.fctr Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr
## 2961          Yes      Science          Yes           NA          Yes
## 3852           NA           NA           NA           NA           NA
##      Q120012.fctr Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr
## 2961          Yes           No       Giving           No          Yes
## 3852           NA           NA           NA          Yes           No
##      Q118237.fctr Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr
## 2961          Yes           No           Pr           No Standard hours
## 3852           No          Yes           NA           No      Odd hours
##      Q117186.fctr Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr
## 2961   Hot headed           NA           NA           NA           NA
## 3852  Cool headed           No        Happy           No          Yes
##      Q116441.fctr Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr
## 2961           NA           NA           NA           NA        Start
## 3852          Yes           No           NA           NA        Start
##      Q115610.fctr Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr
## 2961           NA           NA           NA           NA           NA
## 3852           NA           NA           Me           NA           NA
##      Q114961.fctr Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr
## 2961           NA           NA           NA           NA           NA
## 3852           NA          Yes           No   Mysterious           No
##      Q113992.fctr Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr
## 2961           NA           NA           NA           NA           NA
## 3852          Yes        Tunes   Technology           NA           No
##      Q112512.fctr Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr
## 2961           NA           NA           NA           NA           NA
## 3852          Yes          Yes           No   Supportive           No
##      Q110740.fctr Q109367.fctr Q109244.fctr.1 Q108950.fctr Q108855.fctr
## 2961           NA           No             No     Cautious       Umm...
## 3852           PC          Yes             No           NA           NA
##      Q108617.fctr Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr
## 2961           No        Space          Yes       Online           No
## 3852           NA           NA           NA    In-person           No
##      Q107869.fctr Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr
## 2961           No          Yes          Yes           Gr           NA
## 3852          Yes          Yes          Yes           Gr          Yes
##      Q106388.fctr Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr
## 2961           NA           NA           NA           NA           NA
## 3852          Yes          Yes           NA           NA           NA
##      Q104996.fctr Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr
## 2961           NA           NA           NA           NA           NA
## 3852           NA           NA           NA           NA           NA
##      Q102289.fctr Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr
## 2961           NA           NA           NA           NA           NA
## 3852          Yes          Own           NA           NA           NA
##      Q100689.fctr Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr
## 2961           NA           NA           NA           NA          NA
## 3852          Yes           No           No          Yes      Check!
##      Q99716.fctr Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr
## 2961          NA          NA          NA          NA          NA
## 3852         Yes          No         Yes         Yes          No
##      Q98197.fctr Q98059.fctr Q98078.fctr Q96024.fctr
## 2961          NA          NA          NA          NA
## 3852          NA          NA          NA          NA
## [1] "min distance(0.9498) pair:"
##      USER_ID Party.fctr Q109244.fctr Q124742.fctr Q124122.fctr
## 1423    1771          D           No           NA           NA
## 4299    5365          R           No           NA           NA
##      Q123621.fctr Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q122120.fctr Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q120650.fctr Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q120012.fctr Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q118237.fctr Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q117186.fctr Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q116441.fctr Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr
## 1423           NA           NA           NA          Yes        Start
## 4299           NA           NA           NA          Yes           NA
##      Q115610.fctr Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr
## 1423          Yes           No           Cs          Yes          Yes
## 4299          Yes          Yes           NA          Yes          Yes
##      Q114961.fctr Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr
## 1423           No          Yes           No          TMI           NA
## 4299           No           No          Yes   Mysterious          Yes
##      Q113992.fctr Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr
## 1423          Yes           NA           NA          Yes          Yes
## 4299           No         Talk       People          Yes           NA
##      Q112512.fctr Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr
## 1423           NA           No          Yes   Supportive           NA
## 4299           NA           NA           NA           NA           NA
##      Q110740.fctr Q109367.fctr Q109244.fctr.1 Q108950.fctr Q108855.fctr
## 1423           NA          Yes             No           NA           NA
## 4299           NA          Yes             No           NA           NA
##      Q108617.fctr Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q107869.fctr Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q106388.fctr Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q104996.fctr Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q102289.fctr Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr
## 1423           NA           NA           NA           NA           NA
## 4299           NA           NA           NA           NA           NA
##      Q100689.fctr Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr
## 1423           NA           NA           NA           NA          NA
## 4299           NA           NA           NA           NA          NA
##      Q99716.fctr Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr
## 1423          NA          NA          NA          NA          NA
## 4299          NA          NA          NA          NA          NA
##      Q98197.fctr Q98059.fctr Q98078.fctr Q96024.fctr
## 1423          NA          NA          NA          NA
## 4299          NA          NA          NA          NA
##   Q109244.fctr .clusterid Q109244.fctr.clusterid   D   R  .entropy .knt
## 1           No          1                   No_1 388 704 0.6506727 1092
## 2           No          2                   No_2 440 400 0.6920130  840
## 3           No          3                   No_3 210 317 0.6723914  527
## [1] "glbObsAll$Q109244.fctr$.clusterid Entropy: 0.6694 (98.3085 pct)"
##                      label step_major step_minor label_minor     bgn
## 13            cluster.data          5          0           0 199.194
## 14 partition.data.training          6          0           0 270.858
##        end elapsed
## 13 270.857  71.664
## 14      NA      NA

Step 6.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.10 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.10 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
## 
##     cluster
## [1] "lclgetMatrixCorrelation: duration: 10.438000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 5.073000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 9.930000 secs"
## [1] "cor of Fit vs. New: 1.0000"
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 25.88 secs"
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA      622
## Fit            830             1136       NA
## OOB            208              285       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.4221770        0.5778230       NA
## OOB      0.4219067        0.5780933       NA
##   Q109244.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 1           No   1966    493    622              1              1
##   .freqRatio.Tst
## 1              1
## [1] "glbObsAll: "
## [1] 3081  222
## [1] "glbObsTrn: "
## [1] 2459  222
## [1] "glbObsFit: "
## [1] 1966  221
## [1] "glbObsOOB: "
## [1] 493 221
## [1] "glbObsNew: "
## [1] 622 221
## [1] "partition.data.training chunk: teardown: elapsed: 26.49 secs"
##                      label step_major step_minor label_minor     bgn
## 14 partition.data.training          6          0           0 270.858
## 15         select.features          7          0           0 297.456
##        end elapsed
## 14 297.455  26.597
## 15      NA      NA

Step 7.0: select features

```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
## [1] "cor(.clusterid.fctr, Q113181.fctr)=-0.7180"
## [1] "cor(Party.fctr, .clusterid.fctr)=-0.0640"
## [1] "cor(Party.fctr, Q113181.fctr)=0.0984"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified .clusterid.fctr as highly correlated with
## Q113181.fctr
##                         cor.y exclude.as.feat    cor.y.abs   cor.high.X
## Q115611.fctr     0.1061227024               0 0.1061227024         <NA>
## Q113181.fctr     0.0984260825               0 0.0984260825         <NA>
## Q98197.fctr      0.0740068938               0 0.0740068938         <NA>
## Q116881.fctr     0.0562595864               0 0.0562595864         <NA>
## Q108855.fctr     0.0552557062               0 0.0552557062         <NA>
## Q106272.fctr     0.0508423733               0 0.0508423733         <NA>
## Q122771.fctr     0.0506936997               0 0.0506936997         <NA>
## Q123621.fctr     0.0496371444               0 0.0496371444         <NA>
## Q106388.fctr     0.0494091927               0 0.0494091927         <NA>
## Q110740.fctr     0.0475251274               0 0.0475251274         <NA>
## USER_ID          0.0451253514               1 0.0451253514         <NA>
## .pos             0.0449751220               1 0.0449751220         <NA>
## Q122769.fctr     0.0357238967               0 0.0357238967         <NA>
## Q120472.fctr     0.0353553175               0 0.0353553175         <NA>
## Q101596.fctr     0.0310707949               0 0.0310707949         <NA>
## Q119334.fctr     0.0307545744               0 0.0307545744         <NA>
## Q114152.fctr     0.0298031360               0 0.0298031360         <NA>
## Q98869.fctr      0.0279837842               0 0.0279837842         <NA>
## Q115899.fctr     0.0275355442               0 0.0275355442         <NA>
## Q116797.fctr     0.0264023055               0 0.0264023055         <NA>
## YOB.Age.dff      0.0263896332               0 0.0263896332         <NA>
## Q118232.fctr     0.0263557469               0 0.0263557469         <NA>
## Gender.fctr      0.0260785749               0 0.0260785749         <NA>
## Q105655.fctr     0.0254502518               0 0.0254502518         <NA>
## Q99480.fctr      0.0241968063               0 0.0241968063         <NA>
## Q123464.fctr     0.0232487747               0 0.0232487747         <NA>
## Q120650.fctr     0.0228127973               0 0.0228127973         <NA>
## Q122120.fctr     0.0226963845               0 0.0226963845         <NA>
## Q107869.fctr     0.0218682393               0 0.0218682393         <NA>
## Q120014.fctr     0.0202731591               0 0.0202731591         <NA>
## Q102289.fctr     0.0192468615               0 0.0192468615         <NA>
## Income.fctr      0.0179840418               0 0.0179840418         <NA>
## Q122770.fctr     0.0174735493               0 0.0174735493         <NA>
## Q111580.fctr     0.0170269417               0 0.0170269417         <NA>
## Q116601.fctr     0.0159184435               0 0.0159184435         <NA>
## Q117186.fctr     0.0158641235               0 0.0158641235         <NA>
## Q106993.fctr     0.0151551332               0 0.0151551332         <NA>
## Q112270.fctr     0.0147892685               0 0.0147892685         <NA>
## Q101162.fctr     0.0139326027               0 0.0139326027         <NA>
## Q108856.fctr     0.0128750759               0 0.0128750759         <NA>
## Q117193.fctr     0.0114974111               0 0.0114974111         <NA>
## Q116441.fctr     0.0093463969               0 0.0093463969         <NA>
## Q119851.fctr     0.0089549525               0 0.0089549525         <NA>
## Q111848.fctr     0.0085442819               0 0.0085442819         <NA>
## Q98578.fctr      0.0067135887               0 0.0067135887         <NA>
## Q118892.fctr     0.0063006467               0 0.0063006467         <NA>
## Q114386.fctr     0.0057240993               0 0.0057240993         <NA>
## Q120978.fctr     0.0055115231               0 0.0055115231         <NA>
## Q112512.fctr     0.0053167658               0 0.0053167658         <NA>
## Q102674.fctr     0.0050627208               0 0.0050627208         <NA>
## Q96024.fctr      0.0040534729               0 0.0040534729         <NA>
## Q108950.fctr     0.0039412433               0 0.0039412433         <NA>
## Q115610.fctr     0.0037395055               0 0.0037395055         <NA>
## YOB.Age.fctr     0.0031160191               0 0.0031160191         <NA>
## Q112478.fctr     0.0028932765               0 0.0028932765         <NA>
## Q116197.fctr     0.0026906806               0 0.0026906806         <NA>
## Q124742.fctr     0.0025316261               0 0.0025316261         <NA>
## Q106389.fctr     0.0020182255               0 0.0020182255         <NA>
## Edn.fctr         0.0013569584               0 0.0013569584         <NA>
## Q118117.fctr     0.0006385446               0 0.0006385446         <NA>
## Q100562.fctr     0.0001743827               0 0.0001743827         <NA>
## Q107491.fctr    -0.0001103153               0 0.0001103153         <NA>
## Q116448.fctr    -0.0023584430               0 0.0023584430         <NA>
## Q108754.fctr    -0.0027742157               0 0.0027742157         <NA>
## Q116953.fctr    -0.0029373549               0 0.0029373549         <NA>
## Q115602.fctr    -0.0031238519               0 0.0031238519         <NA>
## Q118233.fctr    -0.0033273008               0 0.0033273008         <NA>
## Q120012.fctr    -0.0039513241               0 0.0039513241         <NA>
## Q118237.fctr    -0.0043335513               0 0.0043335513         <NA>
## Q99581.fctr     -0.0046486977               0 0.0046486977         <NA>
## .rnorm          -0.0048723001               0 0.0048723001         <NA>
## Q120194.fctr    -0.0057263432               0 0.0057263432         <NA>
## Q115777.fctr    -0.0059804934               0 0.0059804934         <NA>
## Q106997.fctr    -0.0063914109               0 0.0063914109         <NA>
## Q100680.fctr    -0.0072431931               0 0.0072431931         <NA>
## Q113584.fctr    -0.0076436688               0 0.0076436688         <NA>
## Q108343.fctr    -0.0079333386               0 0.0079333386         <NA>
## Q121700.fctr    -0.0087942115               0 0.0087942115         <NA>
## Q105840.fctr    -0.0088034036               0 0.0088034036         <NA>
## Q120379.fctr    -0.0089842116               0 0.0089842116         <NA>
## Q103293.fctr    -0.0090167793               0 0.0090167793         <NA>
## Q124122.fctr    -0.0099503887               0 0.0099503887         <NA>
## Q109367.fctr    -0.0100116070               0 0.0100116070         <NA>
## Q113992.fctr    -0.0100378101               0 0.0100378101         <NA>
## Q121699.fctr    -0.0121369662               0 0.0121369662         <NA>
## Q121011.fctr    -0.0122186222               0 0.0122186222         <NA>
## Q114748.fctr    -0.0128363203               0 0.0128363203         <NA>
## Q106042.fctr    -0.0135167901               0 0.0135167901         <NA>
## Q111220.fctr    -0.0145971279               0 0.0145971279         <NA>
## Q114517.fctr    -0.0148538356               0 0.0148538356         <NA>
## YOB             -0.0169432580               1 0.0169432580         <NA>
## Q102687.fctr    -0.0169904229               0 0.0169904229         <NA>
## Q102906.fctr    -0.0173704239               0 0.0173704239         <NA>
## Q98078.fctr     -0.0177772661               0 0.0177772661         <NA>
## Q115390.fctr    -0.0196547694               0 0.0196547694         <NA>
## Q102089.fctr    -0.0200451075               0 0.0200451075         <NA>
## Q100010.fctr    -0.0208031518               0 0.0208031518         <NA>
## Q99982.fctr     -0.0208604939               0 0.0208604939         <NA>
## Q113583.fctr    -0.0211296876               0 0.0211296876         <NA>
## Q108342.fctr    -0.0211946324               0 0.0211946324         <NA>
## Q104996.fctr    -0.0218776356               0 0.0218776356         <NA>
## Q119650.fctr    -0.0222628005               0 0.0222628005         <NA>
## Q100689.fctr    -0.0263249102               0 0.0263249102         <NA>
## Q108617.fctr    -0.0285334447               0 0.0285334447         <NA>
## Q115195.fctr    -0.0295831061               0 0.0295831061         <NA>
## Q99716.fctr     -0.0333178411               0 0.0333178411         <NA>
## Q101163.fctr    -0.0349739760               0 0.0349739760         <NA>
## Q98059.fctr     -0.0354482758               0 0.0354482758         <NA>
## Q114961.fctr    -0.0396043459               0 0.0396043459         <NA>
## .clusterid      -0.0640119129               1 0.0640119129         <NA>
## .clusterid.fctr -0.0640119129               0 0.0640119129 Q113181.fctr
## Hhold.fctr      -0.0644984804               0 0.0644984804         <NA>
## Q109244.fctr               NA               0           NA         <NA>
##                 freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Q115611.fctr     1.346501    0.12200081   FALSE FALSE            FALSE
## Q113181.fctr     1.207806    0.12200081   FALSE FALSE            FALSE
## Q98197.fctr      1.293258    0.12200081   FALSE FALSE            FALSE
## Q116881.fctr     2.244592    0.12200081   FALSE FALSE            FALSE
## Q108855.fctr     1.511876    0.12200081   FALSE FALSE            FALSE
## Q106272.fctr     2.712785    0.12200081   FALSE FALSE            FALSE
## Q122771.fctr     3.656388    0.12200081   FALSE FALSE            FALSE
## Q123621.fctr     1.144186    0.12200081   FALSE FALSE            FALSE
## Q106388.fctr     2.478261    0.12200081   FALSE FALSE            FALSE
## Q110740.fctr     1.491409    0.12200081   FALSE FALSE            FALSE
## USER_ID          1.000000  100.00000000   FALSE FALSE            FALSE
## .pos             1.000000  100.00000000   FALSE FALSE            FALSE
## Q122769.fctr     1.693260    0.12200081   FALSE FALSE            FALSE
## Q120472.fctr     2.803119    0.12200081   FALSE FALSE            FALSE
## Q101596.fctr     1.737877    0.12200081   FALSE FALSE            FALSE
## Q119334.fctr     1.125395    0.12200081   FALSE FALSE            FALSE
## Q114152.fctr     2.270531    0.12200081   FALSE FALSE            FALSE
## Q98869.fctr      3.614191    0.12200081   FALSE FALSE            FALSE
## Q115899.fctr     1.448234    0.12200081   FALSE FALSE            FALSE
## Q116797.fctr     2.093023    0.12200081   FALSE FALSE            FALSE
## YOB.Age.dff      1.021687    0.73200488   FALSE FALSE            FALSE
## Q118232.fctr     1.307600    0.12200081   FALSE FALSE            FALSE
## Gender.fctr      2.405063    0.12200081   FALSE FALSE            FALSE
## Q105655.fctr     1.274947    0.12200081   FALSE FALSE            FALSE
## Q99480.fctr      4.095588    0.12200081   FALSE FALSE            FALSE
## Q123464.fctr     3.190731    0.12200081   FALSE FALSE            FALSE
## Q120650.fctr     3.544379    0.12200081   FALSE FALSE            FALSE
## Q122120.fctr     3.098563    0.12200081   FALSE FALSE            FALSE
## Q107869.fctr     1.293617    0.12200081   FALSE FALSE            FALSE
## Q120014.fctr     1.623324    0.12200081   FALSE FALSE            FALSE
## Q102289.fctr     2.286846    0.12200081   FALSE FALSE            FALSE
## Income.fctr      1.026596    0.28466856   FALSE FALSE            FALSE
## Q122770.fctr     1.407720    0.12200081   FALSE FALSE            FALSE
## Q111580.fctr     1.934936    0.12200081   FALSE FALSE            FALSE
## Q116601.fctr     3.948235    0.12200081   FALSE FALSE            FALSE
## Q117186.fctr     1.761702    0.12200081   FALSE FALSE            FALSE
## Q106993.fctr     4.903581    0.12200081   FALSE FALSE            FALSE
## Q112270.fctr     1.134313    0.12200081   FALSE FALSE            FALSE
## Q101162.fctr     1.556382    0.12200081   FALSE FALSE            FALSE
## Q108856.fctr     2.251156    0.12200081   FALSE FALSE            FALSE
## Q117193.fctr     1.346618    0.12200081   FALSE FALSE            FALSE
## Q116441.fctr     1.638601    0.12200081   FALSE FALSE            FALSE
## Q119851.fctr     1.575758    0.12200081   FALSE FALSE            FALSE
## Q111848.fctr     1.402247    0.12200081   FALSE FALSE            FALSE
## Q98578.fctr      1.717158    0.12200081   FALSE FALSE            FALSE
## Q118892.fctr     1.411356    0.12200081   FALSE FALSE            FALSE
## Q114386.fctr     1.426366    0.12200081   FALSE FALSE            FALSE
## Q120978.fctr     1.264000    0.12200081   FALSE FALSE            FALSE
## Q112512.fctr     4.175743    0.12200081   FALSE FALSE            FALSE
## Q102674.fctr     1.849030    0.12200081   FALSE FALSE            FALSE
## Q96024.fctr      1.683444    0.12200081   FALSE FALSE             TRUE
## Q108950.fctr     2.087366    0.12200081   FALSE FALSE             TRUE
## Q115610.fctr     3.966825    0.12200081   FALSE FALSE             TRUE
## YOB.Age.fctr     1.180000    0.36600244   FALSE FALSE             TRUE
## Q112478.fctr     1.459880    0.12200081   FALSE FALSE             TRUE
## Q116197.fctr     2.031484    0.12200081   FALSE FALSE             TRUE
## Q124742.fctr     1.322468    0.12200081   FALSE FALSE             TRUE
## Q106389.fctr     1.086957    0.12200081   FALSE FALSE             TRUE
## Edn.fctr         1.694524    0.32533550   FALSE FALSE             TRUE
## Q118117.fctr     1.402074    0.12200081   FALSE FALSE             TRUE
## Q100562.fctr     4.329082    0.12200081   FALSE FALSE             TRUE
## Q107491.fctr     6.288591    0.12200081   FALSE FALSE             TRUE
## Q116448.fctr     1.352326    0.12200081   FALSE FALSE             TRUE
## Q108754.fctr     2.045897    0.12200081   FALSE FALSE             TRUE
## Q116953.fctr     1.978788    0.12200081   FALSE FALSE             TRUE
## Q115602.fctr     3.864608    0.12200081   FALSE FALSE             TRUE
## Q118233.fctr     2.654676    0.12200081   FALSE FALSE             TRUE
## Q120012.fctr     1.344262    0.12200081   FALSE FALSE             TRUE
## Q118237.fctr     1.429419    0.12200081   FALSE FALSE             TRUE
## Q99581.fctr      4.989011    0.12200081   FALSE FALSE             TRUE
## .rnorm           1.000000  100.00000000   FALSE FALSE            FALSE
## Q120194.fctr     1.317536    0.12200081   FALSE FALSE            FALSE
## Q115777.fctr     1.387173    0.12200081   FALSE FALSE            FALSE
## Q106997.fctr     1.187117    0.12200081   FALSE FALSE            FALSE
## Q100680.fctr     1.250542    0.12200081   FALSE FALSE            FALSE
## Q113584.fctr     1.008824    0.12200081   FALSE FALSE            FALSE
## Q108343.fctr     1.565774    0.12200081   FALSE FALSE            FALSE
## Q121700.fctr     3.968468    0.12200081   FALSE FALSE            FALSE
## Q105840.fctr     1.486260    0.12200081   FALSE FALSE            FALSE
## Q120379.fctr     1.447596    0.12200081   FALSE FALSE            FALSE
## Q103293.fctr     1.252910    0.12200081   FALSE FALSE            FALSE
## Q124122.fctr     1.187500    0.12200081   FALSE FALSE            FALSE
## Q109367.fctr     1.460805    0.12200081   FALSE FALSE            FALSE
## Q113992.fctr     2.253086    0.12200081   FALSE FALSE            FALSE
## Q121699.fctr     2.600355    0.12200081   FALSE FALSE            FALSE
## Q121011.fctr     1.158969    0.12200081   FALSE FALSE            FALSE
## Q114748.fctr     1.344482    0.12200081   FALSE FALSE            FALSE
## Q106042.fctr     1.319690    0.12200081   FALSE FALSE            FALSE
## Q111220.fctr     3.014898    0.12200081   FALSE FALSE            FALSE
## Q114517.fctr     2.182796    0.12200081   FALSE FALSE            FALSE
## YOB              1.105769    2.92801952   FALSE FALSE            FALSE
## Q102687.fctr     1.004803    0.12200081   FALSE FALSE            FALSE
## Q102906.fctr     1.950213    0.12200081   FALSE FALSE            FALSE
## Q98078.fctr      1.590206    0.12200081   FALSE FALSE            FALSE
## Q115390.fctr     1.460576    0.12200081   FALSE FALSE            FALSE
## Q102089.fctr     2.381260    0.12200081   FALSE FALSE            FALSE
## Q100010.fctr     3.962529    0.12200081   FALSE FALSE            FALSE
## Q99982.fctr      1.107143    0.12200081   FALSE FALSE            FALSE
## Q113583.fctr     1.881690    0.12200081   FALSE FALSE            FALSE
## Q108342.fctr     2.426563    0.12200081   FALSE FALSE            FALSE
## Q104996.fctr     1.032350    0.12200081   FALSE FALSE            FALSE
## Q119650.fctr     3.108830    0.12200081   FALSE FALSE            FALSE
## Q100689.fctr     1.440137    0.12200081   FALSE FALSE            FALSE
## Q108617.fctr     7.888446    0.12200081   FALSE FALSE            FALSE
## Q115195.fctr     1.713147    0.12200081   FALSE FALSE            FALSE
## Q99716.fctr      4.852332    0.12200081   FALSE FALSE            FALSE
## Q101163.fctr     1.437576    0.12200081   FALSE FALSE            FALSE
## Q98059.fctr      5.379888    0.12200081   FALSE FALSE            FALSE
## Q114961.fctr     1.029000    0.12200081   FALSE FALSE            FALSE
## .clusterid       1.300000    0.12200081   FALSE FALSE            FALSE
## .clusterid.fctr  1.300000    0.12200081   FALSE FALSE            FALSE
## Hhold.fctr       1.209330    0.28466856   FALSE FALSE            FALSE
## Q109244.fctr     0.000000    0.04066694    TRUE  TRUE               NA
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

##              cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr    NA               0        NA       <NA>         0
##              percentUnique zeroVar  nzv is.cor.y.abs.low
## Q109244.fctr    0.04066694    TRUE TRUE               NA
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024            .lcn 
##             528             550             622
## [1] "glb_feats_df:"
## [1] 113  12
##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE
##                    id      cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID 0.04512535            TRUE 0.04512535       <NA>
## Party.fctr Party.fctr         NA            TRUE         NA       <NA>
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                <NA>                   NA       FALSE   TRUE
## Party.fctr             <NA>                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor     bgn     end
## 15 select.features          7          0           0 297.456 302.156
## 16      fit.models          8          0           0 302.157      NA
##    elapsed
## 15     4.7
## 16      NA

Step 8.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 303.198  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

glbgetModelSelectFormula <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

glbgetDisplayModelsDf <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#glbgetDisplayModelsDf()

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 303.198 303.234
## 2 fit.models_0_MFO          1          1 myMFO_classfr 303.234      NA
##   elapsed
## 1   0.036
## 2      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.445000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
##        R        D 
## 0.577823 0.422177 
## [1] "MFO.val:"
## [1] "R"
## [1] "myfit_mdl: train complete: 1.033000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 1.037000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##          D        R
## 1 0.577823 0.422177
## 2 0.577823 0.422177
## 3 0.577823 0.422177
## 4 0.577823 0.422177
## 5 0.577823 0.422177
## 6 0.577823 0.422177

##          Prediction
## Reference    D    R
##         D    0  830
##         R    0 1136
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.778230e-01   0.000000e+00   5.556337e-01   5.997798e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   5.095795e-01  4.407827e-182 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##          D        R
## 1 0.577823 0.422177
## 2 0.577823 0.422177
## 3 0.577823 0.422177
## 4 0.577823 0.422177
## 5 0.577823 0.422177
## 6 0.577823 0.422177
##          Prediction
## Reference   D   R
##         D   0 208
##         R   0 285
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780933e-01   0.000000e+00   5.331249e-01   6.221196e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   5.191262e-01   1.022216e-46 
## [1] "myfit_mdl: predict complete: 7.162000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                       0.58
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.4       0.7324307         0.577823
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5556337             0.5997798             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.4       0.7326478        0.5780933
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5331249             0.6221196             0
## [1] "in MFO.Classifier$prob"
##          D        R
## 1 0.577823 0.422177
## 2 0.577823 0.422177
## 3 0.577823 0.422177
## 4 0.577823 0.422177
## 5 0.577823 0.422177
## 6 0.577823 0.422177
## [1] "myfit_mdl: exit: 7.214000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 303.234
## 3 fit.models_0_Random          1          2 myrandom_classfr 310.455
##       end elapsed
## 2 310.454   7.221
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.423000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.824000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used

## [1] "myfit_mdl: train diagnostics complete: 0.827000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference    D    R
##         D    0  830
##         R    0 1136
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.778230e-01   0.000000e+00   5.556337e-01   5.997798e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   5.095795e-01  4.407827e-182 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   D   R
##         D   0 208
##         R   0 285
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780933e-01   0.000000e+00   5.331249e-01   6.221196e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   5.191262e-01   1.022216e-46 
## [1] "myfit_mdl: predict complete: 7.248000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.397                 0.002       0.5127577
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4313253    0.5941901       0.4952189                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7324307         0.577823             0.5556337
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5997798             0       0.4988023    0.4326923
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.5649123       0.5142038                    0.4       0.7326478
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5780933             0.5331249             0.6221196
##   max.Kappa.OOB
## 1             0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 8.058000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 310.455 318.526   8.072
## 4 318.527      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr"
## [1] "myfit_mdl: setup complete: 0.684000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
## 
##     expand
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.00155 on full training set
## [1] "myfit_mdl: train complete: 1.498000 secs"
##   alpha      lambda
## 1   0.1 0.001547307

##             Length Class      Mode     
## a0           47    -none-     numeric  
## beta        188    dgCMatrix  S4       
## df           47    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       47    -none-     numeric  
## dev.ratio    47    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##     (Intercept)  Q113181.fctrNo Q113181.fctrYes  Q115611.fctrNo 
##       0.2769638      -0.2766773       0.3638715      -0.2172004 
## Q115611.fctrYes 
##       0.4047733 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"     "Q113181.fctrNo"  "Q113181.fctrYes" "Q115611.fctrNo" 
## [5] "Q115611.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.606000 secs"

##          Prediction
## Reference   D   R
##         D 385 445
##         R 304 832
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.190234e-01   2.008451e-01   5.971380e-01   6.405531e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   1.108701e-04   3.129297e-07

##          Prediction
## Reference   D   R
##         D   0 208
##         R   0 285
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780933e-01   0.000000e+00   5.331249e-01   6.221196e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   5.191262e-01   1.022216e-46 
## [1] "myfit_mdl: predict complete: 7.307000 secs"
##                           id                     feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q115611.fctr,Q113181.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.807                 0.029       0.5770809
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.3566265    0.7975352       0.6231477                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1        0.689598        0.6190234              0.597138
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.6405531     0.2008451        0.533249    0.2980769
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.7684211       0.5660003                    0.4       0.7326478
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5780933             0.5331249             0.6221196
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 7.376000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr"
## [1] "myfit_mdl: setup complete: 0.689000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0 on full training set
## [1] "myfit_mdl: train complete: 2.243000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 1966 
## 
##            CP nsplit rel error
## 1 0.043975904      0 1.0000000
## 2 0.004819277      2 0.9120482
## 3 0.000000000      4 0.9024096
## 
## Variable importance
## Q113181.fctrYes  Q113181.fctrNo  Q115611.fctrNo Q115611.fctrYes 
##              34              29              20              17 
## 
## Node number 1: 1966 observations,    complexity param=0.0439759
##   predicted class=R  expected loss=0.422177  P(node) =1
##     class counts:   830  1136
##    probabilities: 0.422 0.578 
##   left son=2 (1203 obs) right son=3 (763 obs)
##   Primary splits:
##       Q113181.fctrYes < 0.5 to the left,  improve=23.53459, (0 missing)
##       Q115611.fctrYes < 0.5 to the left,  improve=22.57049, (0 missing)
##       Q113181.fctrNo  < 0.5 to the right, improve=20.68997, (0 missing)
##       Q115611.fctrNo  < 0.5 to the right, improve=18.43308, (0 missing)
##   Surrogate splits:
##       Q113181.fctrNo  < 0.5 to the right, agree=0.853, adj=0.621, (0 split)
##       Q115611.fctrYes < 0.5 to the left,  agree=0.614, adj=0.005, (0 split)
## 
## Node number 2: 1203 observations,    complexity param=0.0439759
##   predicted class=R  expected loss=0.4837905  P(node) =0.6119023
##     class counts:   582   621
##    probabilities: 0.484 0.516 
##   left son=4 (609 obs) right son=5 (594 obs)
##   Primary splits:
##       Q115611.fctrNo  < 0.5 to the right, improve=14.301960, (0 missing)
##       Q115611.fctrYes < 0.5 to the left,  improve=14.298210, (0 missing)
##       Q113181.fctrNo  < 0.5 to the right, improve= 1.999318, (0 missing)
##   Surrogate splits:
##       Q115611.fctrYes < 0.5 to the left,  agree=0.800, adj=0.596, (0 split)
##       Q113181.fctrNo  < 0.5 to the right, agree=0.608, adj=0.207, (0 split)
## 
## Node number 3: 763 observations
##   predicted class=R  expected loss=0.3250328  P(node) =0.3880977
##     class counts:   248   515
##    probabilities: 0.325 0.675 
## 
## Node number 4: 609 observations
##   predicted class=D  expected loss=0.4400657  P(node) =0.309766
##     class counts:   341   268
##    probabilities: 0.560 0.440 
## 
## Node number 5: 594 observations,    complexity param=0.004819277
##   predicted class=R  expected loss=0.4057239  P(node) =0.3021363
##     class counts:   241   353
##    probabilities: 0.406 0.594 
##   left son=10 (240 obs) right son=11 (354 obs)
##   Primary splits:
##       Q115611.fctrYes < 0.5 to the left,  improve=2.9913600, (0 missing)
##       Q113181.fctrNo  < 0.5 to the right, improve=0.1904018, (0 missing)
##   Surrogate splits:
##       Q113181.fctrNo < 0.5 to the left,  agree=0.788, adj=0.475, (0 split)
## 
## Node number 10: 240 observations,    complexity param=0.004819277
##   predicted class=R  expected loss=0.4666667  P(node) =0.1220753
##     class counts:   112   128
##    probabilities: 0.467 0.533 
##   left son=20 (80 obs) right son=21 (160 obs)
##   Primary splits:
##       Q113181.fctrNo < 0.5 to the right, improve=1.666667, (0 missing)
## 
## Node number 11: 354 observations
##   predicted class=R  expected loss=0.3644068  P(node) =0.180061
##     class counts:   129   225
##    probabilities: 0.364 0.636 
## 
## Node number 20: 80 observations
##   predicted class=D  expected loss=0.45  P(node) =0.04069176
##     class counts:    44    36
##    probabilities: 0.550 0.450 
## 
## Node number 21: 160 observations
##   predicted class=R  expected loss=0.425  P(node) =0.08138352
##     class counts:    68    92
##    probabilities: 0.425 0.575 
## 
## n= 1966 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 1966 830 R (0.4221770 0.5778230)  
##    2) Q113181.fctrYes< 0.5 1203 582 R (0.4837905 0.5162095)  
##      4) Q115611.fctrNo>=0.5 609 268 D (0.5599343 0.4400657) *
##      5) Q115611.fctrNo< 0.5 594 241 R (0.4057239 0.5942761)  
##       10) Q115611.fctrYes< 0.5 240 112 R (0.4666667 0.5333333)  
##         20) Q113181.fctrNo>=0.5 80  36 D (0.5500000 0.4500000) *
##         21) Q113181.fctrNo< 0.5 160  68 R (0.4250000 0.5750000) *
##       11) Q115611.fctrYes>=0.5 354 129 R (0.3644068 0.6355932) *
##    3) Q113181.fctrYes>=0.5 763 248 R (0.3250328 0.6749672) *
## [1] "myfit_mdl: train diagnostics complete: 2.953000 secs"

##          Prediction
## Reference   D   R
##         D 385 445
##         R 304 832
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.190234e-01   2.008451e-01   5.971380e-01   6.405531e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   1.108701e-04   3.129297e-07

##          Prediction
## Reference   D   R
##         D   0 208
##         R   0 285
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780933e-01   0.000000e+00   5.331249e-01   6.221196e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   5.191262e-01   1.022216e-46 
## [1] "myfit_mdl: predict complete: 8.777000 secs"
##                     id                     feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q115611.fctr,Q113181.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.549                 0.012       0.5981249
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4638554    0.7323944       0.6123128                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1        0.689598         0.617493              0.597138
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.6405531     0.1964223       0.5326586    0.3846154
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6807018       0.5440283                    0.4       0.7326478
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5780933             0.5331249             0.6221196
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0         0.01545615      0.03240346
## [1] "myfit_mdl: exit: 8.839000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##       bgn     end elapsed
## 4 318.527 334.784  16.257
## 5 334.785      NA      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q115611.fctr:Q113181.fctr"
## [1] "myfit_mdl: setup complete: 0.689000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0017 on full training set
## [1] "myfit_mdl: train complete: 2.939000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0           71    -none-     numeric  
## beta        568    dgCMatrix  S4       
## df           71    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       71    -none-     numeric  
## dev.ratio    71    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        8    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                  Q113181.fctrNo 
##                       0.2977129                      -0.4468599 
##                 Q113181.fctrYes                  Q115611.fctrNo 
##                       0.4761540                      -0.4110267 
##                 Q115611.fctrYes   Q113181.fctrNo:Q115611.fctrNo 
##                       0.6378051                       0.3051491 
##  Q113181.fctrYes:Q115611.fctrNo Q113181.fctrYes:Q115611.fctrYes 
##                       0.1455212                      -0.4588794 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                  Q113181.fctrNo 
##                       0.2984673                      -0.4519473 
##                 Q113181.fctrYes                  Q115611.fctrNo 
##                       0.4794180                      -0.4156572 
##                 Q115611.fctrYes   Q113181.fctrNo:Q115611.fctrNo 
##                       0.6424388                       0.3143260 
##  Q113181.fctrYes:Q115611.fctrNo Q113181.fctrYes:Q115611.fctrYes 
##                       0.1463672                      -0.4679212 
## [1] "myfit_mdl: train diagnostics complete: 3.594000 secs"

##          Prediction
## Reference   D   R
##         D 385 445
##         R 304 832
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.190234e-01   2.008451e-01   5.971380e-01   6.405531e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   1.108701e-04   3.129297e-07

##          Prediction
## Reference   D   R
##         D   0 208
##         R   0 285
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780933e-01   0.000000e+00   5.331249e-01   6.221196e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   5.191262e-01   1.022216e-46 
## [1] "myfit_mdl: predict complete: 9.323000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                 feats max.nTuningRuns
## 1 Q115611.fctr,Q113181.fctr,Q115611.fctr:Q113181.fctr              25
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      2.243                 0.055       0.5981249
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4638554    0.7323944       0.6231477                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1        0.689598        0.6168144              0.597138
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.6405531     0.1938108       0.5326586    0.3846154
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6807018       0.5660003                    0.4       0.7326478
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5780933             0.5331249             0.6221196
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0         0.01509263      0.03210463
## [1] "myfit_mdl: exit: 9.395000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##       bgn     end elapsed
## 5 334.785 344.193   9.408
## 6 344.193      NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.709000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0344 on full training set
## [1] "myfit_mdl: train complete: 15.776000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             79  -none-     numeric  
## beta        19592  dgCMatrix  S4       
## df             79  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         79  -none-     numeric  
## dev.ratio      79  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        248  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                    0.2633318704                    0.0320158828 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                   -0.2202580490                   -0.0318781202 
##                 Q101163.fctrDad                  Q106272.fctrNo 
##                    0.1430746427                   -0.0450052896 
##                  Q106997.fctrYy                Q108855.fctrYes! 
##                   -0.0002262395                    0.0427343392 
##                  Q110740.fctrPC                  Q113181.fctrNo 
##                    0.0416540315                   -0.1671631314 
##                 Q113181.fctrYes                  Q115611.fctrNo 
##                    0.1309369866                   -0.1744318522 
##                 Q115611.fctrYes                  Q115899.fctrCs 
##                    0.1690463888                   -0.0132375194 
##               Q116881.fctrHappy               Q116881.fctrRight 
##                   -0.0063713307                    0.1174127433 
##                  Q116953.fctrNo                  Q120379.fctrNo 
##                    0.0250992146                    0.0045916356 
##             Q120472.fctrScience                   Q98197.fctrNo 
##                    0.0609351200                   -0.0386164154 
##                  Q98197.fctrYes                   Q98869.fctrNo 
##                    0.0697602827                   -0.1493069928 
##                   Q99480.fctrNo                  YOB.Age.fctr^6 
##                   -0.0973638913                   -0.0288455784 
## Q109244.fctrNo:.clusterid.fctr2 
##                   -0.0931887807 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                    0.2609630670                    0.0402422204 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                   -0.2561411259                   -0.0993139999 
##                 Q101163.fctrDad                  Q106272.fctrNo 
##                    0.1542207150                   -0.0534914847 
##                  Q106997.fctrYy                Q108855.fctrYes! 
##                   -0.0196332841                    0.0567852935 
##                  Q110740.fctrPC                  Q113181.fctrNo 
##                    0.0549141024                   -0.1750094841 
##                 Q113181.fctrYes                 Q115195.fctrYes 
##                    0.1330889609                   -0.0034194419 
##                  Q115611.fctrNo                 Q115611.fctrYes 
##                   -0.1815551426                    0.1657421960 
##                  Q115899.fctrCs               Q116881.fctrHappy 
##                   -0.0263626990                   -0.0233200721 
##               Q116881.fctrRight                  Q116953.fctrNo 
##                    0.1176101966                    0.0455947006 
##                  Q120379.fctrNo                 Q120379.fctrYes 
##                    0.0154287092                   -0.0003507129 
##             Q120472.fctrScience                   Q98197.fctrNo 
##                    0.0758954098                   -0.0398247287 
##                  Q98197.fctrYes                   Q98869.fctrNo 
##                    0.0743752115                   -0.1678997175 
##                   Q99480.fctrNo                  YOB.Age.fctr^6 
##                   -0.1085844176                   -0.0463361480 
## Q109244.fctrNo:.clusterid.fctr2 
##                   -0.0949497395 
## [1] "myfit_mdl: train diagnostics complete: 16.449000 secs"

##          Prediction
## Reference   D   R
##         D 429 401
##         R 326 810
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.302136e-01   2.327280e-01   6.084387e-01   6.515995e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   1.239263e-06   6.060166e-03

##          Prediction
## Reference   D   R
##         D  20 188
##         R  15 270
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.882353e-01   4.903125e-02   5.433559e-01   6.320501e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   3.415120e-01   1.484209e-33 
## [1] "myfit_mdl: predict complete: 26.138000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     14.978                 1.446
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5828451    0.3144578    0.8512324       0.6581803
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.55       0.6902429        0.6037574
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6084387             0.6515995     0.1307064
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5370867    0.2355769    0.8385965       0.5838394
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.45       0.7267833        0.5882353
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5433559             0.6320501    0.04903125
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01089705      0.02153295
## [1] "myfit_mdl: exit: 26.407000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn     end
## 6 fit.models_0_Low.cor.X          1          5      glmnet 344.193 370.635
## 7       fit.models_0_end          1          6    teardown 370.636      NA
##   elapsed
## 6  26.443
## 7      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn    end elapsed
## 16 fit.models          8          0           0 302.157 370.65  68.493
## 17 fit.models          8          1           1 370.651     NA      NA

```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 375.428  NA      NA
##                label step_major step_minor label_minor     bgn    end
## 1   fit.models_1_bgn          1          0       setup 375.428 375.44
## 2 fit.models_1_All.X          1          1       setup 375.441     NA
##   elapsed
## 1   0.013
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 375.441 375.448
## 3 fit.models_1_All.X          1          2      glmnet 375.449      NA
##   elapsed
## 2   0.007
## 3      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.683000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0344 on full training set
## [1] "myfit_mdl: train complete: 15.784000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             79  -none-     numeric  
## beta        19592  dgCMatrix  S4       
## df             79  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         79  -none-     numeric  
## dev.ratio      79  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        248  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                    0.2633318704                    0.0320158828 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                   -0.2202580490                   -0.0318781202 
##                 Q101163.fctrDad                  Q106272.fctrNo 
##                    0.1430746427                   -0.0450052896 
##                  Q106997.fctrYy                Q108855.fctrYes! 
##                   -0.0002262395                    0.0427343392 
##                  Q110740.fctrPC                  Q113181.fctrNo 
##                    0.0416540315                   -0.1671631314 
##                 Q113181.fctrYes                  Q115611.fctrNo 
##                    0.1309369866                   -0.1744318522 
##                 Q115611.fctrYes                  Q115899.fctrCs 
##                    0.1690463888                   -0.0132375194 
##               Q116881.fctrHappy               Q116881.fctrRight 
##                   -0.0063713307                    0.1174127433 
##                  Q116953.fctrNo                  Q120379.fctrNo 
##                    0.0250992146                    0.0045916356 
##             Q120472.fctrScience                   Q98197.fctrNo 
##                    0.0609351200                   -0.0386164154 
##                  Q98197.fctrYes                   Q98869.fctrNo 
##                    0.0697602827                   -0.1493069928 
##                   Q99480.fctrNo                  YOB.Age.fctr^6 
##                   -0.0973638913                   -0.0288455784 
## Q109244.fctrNo:.clusterid.fctr2 
##                   -0.0931887807 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                    0.2609630670                    0.0402422204 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                   -0.2561411259                   -0.0993139999 
##                 Q101163.fctrDad                  Q106272.fctrNo 
##                    0.1542207150                   -0.0534914847 
##                  Q106997.fctrYy                Q108855.fctrYes! 
##                   -0.0196332841                    0.0567852935 
##                  Q110740.fctrPC                  Q113181.fctrNo 
##                    0.0549141024                   -0.1750094841 
##                 Q113181.fctrYes                 Q115195.fctrYes 
##                    0.1330889609                   -0.0034194419 
##                  Q115611.fctrNo                 Q115611.fctrYes 
##                   -0.1815551426                    0.1657421960 
##                  Q115899.fctrCs               Q116881.fctrHappy 
##                   -0.0263626990                   -0.0233200721 
##               Q116881.fctrRight                  Q116953.fctrNo 
##                    0.1176101966                    0.0455947006 
##                  Q120379.fctrNo                 Q120379.fctrYes 
##                    0.0154287092                   -0.0003507129 
##             Q120472.fctrScience                   Q98197.fctrNo 
##                    0.0758954098                   -0.0398247287 
##                  Q98197.fctrYes                   Q98869.fctrNo 
##                    0.0743752115                   -0.1678997175 
##                   Q99480.fctrNo                  YOB.Age.fctr^6 
##                   -0.1085844176                   -0.0463361480 
## Q109244.fctrNo:.clusterid.fctr2 
##                   -0.0949497395 
## [1] "myfit_mdl: train diagnostics complete: 16.436000 secs"

##          Prediction
## Reference   D   R
##         D 429 401
##         R 326 810
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.302136e-01   2.327280e-01   6.084387e-01   6.515995e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   1.239263e-06   6.060166e-03

##          Prediction
## Reference   D   R
##         D  20 188
##         R  15 270
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.882353e-01   4.903125e-02   5.433559e-01   6.320501e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   3.415120e-01   1.484209e-33 
## [1] "myfit_mdl: predict complete: 26.198000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     15.017                 1.449
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5828451    0.3144578    0.8512324       0.6581803
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.55       0.6902429        0.6037574
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6084387             0.6515995     0.1307064
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5370867    0.2355769    0.8385965       0.5838394
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.45       0.7267833        0.5882353
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5433559             0.6320501    0.04903125
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01089705      0.02153295
## [1] "myfit_mdl: exit: 26.500000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 375.449 401.954
## 4 fit.models_1_All.X          1          3         glm 401.955      NA
##   elapsed
## 3  26.505
## 4      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glm"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.693000 secs"
## + Fold1.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 7.404000 secs"
##   parameter
## 1      none

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1644  -1.0601   0.5681   0.9560   2.1298  
## 
## Coefficients: (8 not defined because of singularities)
##                                     Estimate Std. Error z value Pr(>|z|)
## (Intercept)                         0.991269   0.607579   1.632  0.10278
## .rnorm                             -0.026116   0.052760  -0.495  0.62059
## Edn.fctr.L                         -0.200117   0.269349  -0.743  0.45750
## Edn.fctr.Q                          0.092423   0.243957   0.379  0.70480
## Edn.fctr.C                          0.007956   0.224101   0.036  0.97168
## `Edn.fctr^4`                        0.151112   0.218389   0.692  0.48898
## `Edn.fctr^5`                       -0.133546   0.197684  -0.676  0.49932
## `Edn.fctr^6`                       -0.146765   0.175892  -0.834  0.40405
## `Edn.fctr^7`                        0.147078   0.186185   0.790  0.42956
## Gender.fctrF                        0.011825   0.448105   0.026  0.97895
## Gender.fctrM                       -0.165548   0.444319  -0.373  0.70945
## Hhold.fctrMKn                      -0.383869   0.326940  -1.174  0.24034
## Hhold.fctrMKy                      -0.031037   0.309017  -0.100  0.92000
## Hhold.fctrPKn                      -1.151954   0.431984  -2.667  0.00766
## Hhold.fctrPKy                      -1.553562   0.642533  -2.418  0.01561
## Hhold.fctrSKn                      -0.344671   0.280495  -1.229  0.21915
## Hhold.fctrSKy                      -0.626533   0.419341  -1.494  0.13515
## Income.fctr.L                       0.140293   0.176960   0.793  0.42790
## Income.fctr.Q                       0.213424   0.160257   1.332  0.18294
## Income.fctr.C                       0.457172   0.159223   2.871  0.00409
## `Income.fctr^4`                    -0.050953   0.151499  -0.336  0.73662
## `Income.fctr^5`                     0.120852   0.146113   0.827  0.40817
## `Income.fctr^6`                    -0.300383   0.140671  -2.135  0.03273
## Q100010.fctrNo                     -0.251679   0.415688  -0.605  0.54488
## Q100010.fctrYes                    -0.224876   0.398657  -0.564  0.57270
## Q100562.fctrNo                     -0.137303   0.358919  -0.383  0.70206
## Q100562.fctrYes                     0.083711   0.340153   0.246  0.80561
## Q100680.fctrNo                      0.032573   0.337265   0.097  0.92306
## Q100680.fctrYes                     0.165225   0.331815   0.498  0.61853
## Q100689.fctrNo                     -0.303899   0.366854  -0.828  0.40745
## Q100689.fctrYes                    -0.523249   0.362749  -1.442  0.14917
## Q101162.fctrOptimist                0.775747   0.333537   2.326  0.02003
## Q101162.fctrPessimist               0.726961   0.339510   2.141  0.03226
## Q101163.fctrDad                     0.008980   0.268282   0.033  0.97330
## Q101163.fctrMom                    -0.320727   0.273928  -1.171  0.24166
## Q101596.fctrNo                      0.231840   0.300302   0.772  0.44010
## Q101596.fctrYes                     0.249450   0.308642   0.808  0.41896
## Q102089.fctrOwn                    -0.010962   0.297526  -0.037  0.97061
## Q102089.fctrRent                   -0.114060   0.308644  -0.370  0.71172
## Q102289.fctrNo                     -0.100562   0.293930  -0.342  0.73225
## Q102289.fctrYes                    -0.054354   0.307430  -0.177  0.85966
## Q102674.fctrNo                      0.561044   0.402473   1.394  0.16332
## Q102674.fctrYes                     0.769268   0.411517   1.869  0.06157
## Q102687.fctrNo                     -0.605609   0.424565  -1.426  0.15375
## Q102687.fctrYes                    -0.637051   0.425598  -1.497  0.13444
## Q102906.fctrNo                      0.097487   0.284649   0.342  0.73199
## Q102906.fctrYes                     0.062005   0.292155   0.212  0.83193
## Q103293.fctrNo                     -0.072704   0.267864  -0.271  0.78607
## Q103293.fctrYes                    -0.072809   0.268384  -0.271  0.78617
## Q104996.fctrNo                     -0.347102   0.264119  -1.314  0.18878
## Q104996.fctrYes                    -0.381576   0.265751  -1.436  0.15105
## Q105655.fctrNo                      0.297142   0.303546   0.979  0.32763
## Q105655.fctrYes                     0.455145   0.305384   1.490  0.13612
## Q105840.fctrNo                      0.017017   0.295353   0.058  0.95405
## Q105840.fctrYes                     0.095910   0.303111   0.316  0.75169
## Q106042.fctrNo                     -0.125180   0.297548  -0.421  0.67397
## Q106042.fctrYes                    -0.339360   0.300704  -1.129  0.25909
## Q106272.fctrNo                     -0.537610   0.343165  -1.567  0.11720
## Q106272.fctrYes                    -0.209695   0.331839  -0.632  0.52744
## Q106388.fctrNo                      0.023387   0.407917   0.057  0.95428
## Q106388.fctrYes                     0.108551   0.423464   0.256  0.79769
## Q106389.fctrNo                      0.442991   0.389143   1.138  0.25496
## Q106389.fctrYes                     0.485941   0.391850   1.240  0.21493
## Q106993.fctrNo                      0.275408   0.370709   0.743  0.45753
## Q106993.fctrYes                     0.056690   0.346841   0.163  0.87017
## Q106997.fctrGr                     -0.153672   0.352755  -0.436  0.66310
## Q106997.fctrYy                     -0.461449   0.354946  -1.300  0.19358
## Q107491.fctrNo                      0.017754   0.279028   0.064  0.94927
## Q107491.fctrYes                    -0.006292   0.240852  -0.026  0.97916
## Q107869.fctrNo                      0.089522   0.244395   0.366  0.71414
## Q107869.fctrYes                     0.276595   0.242028   1.143  0.25311
## `Q108342.fctrIn-person`            -0.381403   0.306053  -1.246  0.21269
## Q108342.fctrOnline                 -0.528484   0.313324  -1.687  0.09166
## Q108343.fctrNo                      0.180403   0.324368   0.556  0.57810
## Q108343.fctrYes                     0.157429   0.332446   0.474  0.63582
## Q108617.fctrNo                     -0.491909   0.277115  -1.775  0.07588
## Q108617.fctrYes                    -0.247272   0.325857  -0.759  0.44795
## Q108754.fctrNo                      0.344660   0.297549   1.158  0.24673
## Q108754.fctrYes                     0.287588   0.308441   0.932  0.35113
## Q108855.fctrUmm...                 -0.199600   0.362767  -0.550  0.58217
## `Q108855.fctrYes!`                  0.094827   0.359880   0.263  0.79217
## Q108856.fctrSocialize               0.100722   0.364501   0.276  0.78230
## Q108856.fctrSpace                   0.171245   0.354685   0.483  0.62923
## Q108950.fctrCautious               -0.003292   0.270213  -0.012  0.99028
## `Q108950.fctrRisk-friendly`        -0.144772   0.281463  -0.514  0.60700
## Q109367.fctrNo                      0.221249   0.289110   0.765  0.44411
## Q109367.fctrYes                     0.230805   0.287418   0.803  0.42196
## Q110740.fctrMac                    -0.138829   0.227843  -0.609  0.54231
## Q110740.fctrPC                      0.100075   0.223492   0.448  0.65431
## Q111220.fctrNo                      0.057332   0.246155   0.233  0.81583
## Q111220.fctrYes                    -0.185861   0.259667  -0.716  0.47414
## Q111580.fctrDemanding               0.256024   0.250207   1.023  0.30619
## Q111580.fctrSupportive              0.098008   0.238604   0.411  0.68125
## Q111848.fctrNo                     -0.074464   0.266899  -0.279  0.78025
## Q111848.fctrYes                    -0.127080   0.262682  -0.484  0.62854
## Q112270.fctrNo                     -0.046855   0.245450  -0.191  0.84861
## Q112270.fctrYes                    -0.097845   0.248400  -0.394  0.69366
## Q112478.fctrNo                      0.721439   0.298843   2.414  0.01577
## Q112478.fctrYes                     0.567795   0.293957   1.932  0.05341
## Q112512.fctrNo                     -0.551262   0.315967  -1.745  0.08104
## Q112512.fctrYes                    -0.490866   0.289049  -1.698  0.08947
## Q113181.fctrNo                     -0.296604   0.270014  -1.098  0.27200
## Q113181.fctrYes                     0.000738   0.412023   0.002  0.99857
## Q113583.fctrTalk                   -0.150933   0.370952  -0.407  0.68410
## Q113583.fctrTunes                  -0.104800   0.366011  -0.286  0.77462
## Q113584.fctrPeople                  0.185195   0.380943   0.486  0.62686
## Q113584.fctrTechnology              0.241907   0.379948   0.637  0.52433
## Q113992.fctrNo                      0.009461   0.286656   0.033  0.97367
## Q113992.fctrYes                     0.003153   0.298870   0.011  0.99158
## Q114152.fctrNo                      0.026482   0.265648   0.100  0.92059
## Q114152.fctrYes                    -0.202549   0.281017  -0.721  0.47105
## Q114386.fctrMysterious             -0.092848   0.286260  -0.324  0.74567
## Q114386.fctrTMI                    -0.004572   0.289476  -0.016  0.98740
## Q114517.fctrNo                     -0.862809   0.345739  -2.496  0.01258
## Q114517.fctrYes                    -0.655434   0.355997  -1.841  0.06560
## Q114748.fctrNo                      0.443493   0.350601   1.265  0.20589
## Q114748.fctrYes                     0.424823   0.349327   1.216  0.22394
## Q114961.fctrNo                      0.126472   0.297312   0.425  0.67056
## Q114961.fctrYes                     0.042861   0.294965   0.145  0.88447
## Q115195.fctrNo                     -0.439683   0.302398  -1.454  0.14595
## Q115195.fctrYes                    -0.528359   0.294417  -1.795  0.07272
## Q115390.fctrNo                      0.207017   0.248220   0.834  0.40428
## Q115390.fctrYes                     0.164262   0.239659   0.685  0.49309
## Q115602.fctrNo                     -0.016550   0.335607  -0.049  0.96067
## Q115602.fctrYes                    -0.344335   0.312848  -1.101  0.27105
## Q115610.fctrNo                      0.084190   0.357210   0.236  0.81367
## Q115610.fctrYes                     0.224638   0.337380   0.666  0.50552
## Q115611.fctrNo                      0.007025   0.356191   0.020  0.98426
## Q115611.fctrYes                     0.488383   0.374470   1.304  0.19217
## Q115777.fctrEnd                     0.012074   0.296492   0.041  0.96752
## Q115777.fctrStart                  -0.076337   0.290555  -0.263  0.79276
## Q115899.fctrCs                     -0.368543   0.287158  -1.283  0.19935
## Q115899.fctrMe                     -0.142400   0.282830  -0.503  0.61463
## Q116197.fctrA.M.                    0.126045   0.273254   0.461  0.64460
## Q116197.fctrP.M.                    0.177213   0.262513   0.675  0.49963
## Q116441.fctrNo                      0.115255   0.384738   0.300  0.76451
## Q116441.fctrYes                     0.013157   0.394265   0.033  0.97338
## Q116448.fctrNo                     -0.018798   0.342179  -0.055  0.95619
## Q116448.fctrYes                    -0.053211   0.344872  -0.154  0.87738
## Q116601.fctrNo                     -0.111587   0.355381  -0.314  0.75353
## Q116601.fctrYes                     0.077317   0.329400   0.235  0.81442
## Q116797.fctrNo                      0.191443   0.309536   0.618  0.53626
## Q116797.fctrYes                     0.323770   0.319805   1.012  0.31135
## Q116881.fctrHappy                  -0.507626   0.304706  -1.666  0.09572
## Q116881.fctrRight                  -0.130347   0.316838  -0.411  0.68078
## Q116953.fctrNo                      0.580040   0.316724   1.831  0.06704
## Q116953.fctrYes                     0.174950   0.302570   0.578  0.56312
## `Q117186.fctrCool headed`           0.016804   0.303255   0.055  0.95581
## `Q117186.fctrHot headed`            0.092086   0.309919   0.297  0.76637
## `Q117193.fctrOdd hours`            -0.024769   0.288386  -0.086  0.93155
## `Q117193.fctrStandard hours`        0.075606   0.282990   0.267  0.78934
## Q118117.fctrNo                     -0.100291   0.317549  -0.316  0.75213
## Q118117.fctrYes                    -0.216540   0.317406  -0.682  0.49510
## Q118232.fctrId                     -0.181742   0.269463  -0.674  0.50002
## Q118232.fctrPr                     -0.117636   0.265460  -0.443  0.65767
## Q118233.fctrNo                     -0.831345   0.443194  -1.876  0.06068
## Q118233.fctrYes                    -1.044184   0.453952  -2.300  0.02144
## Q118237.fctrNo                      1.089610   0.451383   2.414  0.01578
## Q118237.fctrYes                     1.015384   0.452768   2.243  0.02492
## Q118892.fctrNo                     -0.246224   0.288553  -0.853  0.39349
## Q118892.fctrYes                    -0.125936   0.284256  -0.443  0.65774
## Q119334.fctrNo                      0.066805   0.270111   0.247  0.80466
## Q119334.fctrYes                     0.128987   0.264976   0.487  0.62641
## Q119650.fctrGiving                  0.239632   0.267602   0.895  0.37053
## Q119650.fctrReceiving               0.201165   0.288351   0.698  0.48540
## Q119851.fctrNo                      0.305315   0.324779   0.940  0.34718
## Q119851.fctrYes                     0.344918   0.328731   1.049  0.29407
## Q120012.fctrNo                     -0.016919   0.348819  -0.049  0.96132
## Q120012.fctrYes                    -0.079042   0.348109  -0.227  0.82038
## Q120014.fctrNo                     -0.204980   0.299233  -0.685  0.49333
## Q120014.fctrYes                    -0.169146   0.296006  -0.571  0.56771
## `Q120194.fctrStudy first`          -0.331644   0.285999  -1.160  0.24621
## `Q120194.fctrTry first`            -0.297702   0.290318  -1.025  0.30516
## Q120379.fctrNo                      0.121968   0.311697   0.391  0.69557
## Q120379.fctrYes                    -0.201618   0.314084  -0.642  0.52092
## Q120472.fctrArt                     0.107977   0.310378   0.348  0.72792
## Q120472.fctrScience                 0.364436   0.293025   1.244  0.21361
## Q120650.fctrNo                      0.219661   0.341400   0.643  0.51996
## Q120650.fctrYes                     0.211490   0.272041   0.777  0.43691
## Q120978.fctrNo                      0.075180   0.318447   0.236  0.81337
## Q120978.fctrYes                    -0.105483   0.315435  -0.334  0.73807
## Q121011.fctrNo                     -0.222398   0.318486  -0.698  0.48499
## Q121011.fctrYes                    -0.203567   0.321108  -0.634  0.52611
## Q121699.fctrNo                     -1.053082   0.564338  -1.866  0.06203
## Q121699.fctrYes                    -1.265869   0.558970  -2.265  0.02353
## Q121700.fctrNo                      1.307652   0.547691   2.388  0.01696
## Q121700.fctrYes                     1.023719   0.572220   1.789  0.07361
## Q122120.fctrNo                     -0.306131   0.282213  -1.085  0.27803
## Q122120.fctrYes                    -0.195951   0.298586  -0.656  0.51165
## Q122769.fctrNo                      0.155830   0.419425   0.372  0.71024
## Q122769.fctrYes                     0.173374   0.425903   0.407  0.68395
## Q122770.fctrNo                     -0.702183   0.591519  -1.187  0.23519
## Q122770.fctrYes                    -0.721058   0.591841  -1.218  0.22310
## Q122771.fctrPc                      1.236948   0.552640   2.238  0.02520
## Q122771.fctrPt                      1.408992   0.566957   2.485  0.01295
## Q123464.fctrNo                     -0.167522   0.273040  -0.614  0.53952
## Q123464.fctrYes                     0.054452   0.370229   0.147  0.88307
## Q123621.fctrNo                     -0.011323   0.275543  -0.041  0.96722
## Q123621.fctrYes                     0.254244   0.275580   0.923  0.35623
## Q124122.fctrNo                     -0.317951   0.229051  -1.388  0.16510
## Q124122.fctrYes                    -0.396971   0.223840  -1.773  0.07615
## Q124742.fctrNo                     -0.090772   0.146243  -0.621  0.53480
## Q124742.fctrYes                     0.160048   0.168621   0.949  0.34254
## Q96024.fctrNo                      -0.061880   0.220550  -0.281  0.77904
## Q96024.fctrYes                     -0.137061   0.210512  -0.651  0.51499
## `Q98059.fctrOnly-child`            -0.278136   0.438231  -0.635  0.52564
## Q98059.fctrYes                     -0.416747   0.397357  -1.049  0.29427
## Q98078.fctrNo                      -0.419189   0.346834  -1.209  0.22681
## Q98078.fctrYes                     -0.338497   0.351357  -0.963  0.33535
## Q98197.fctrNo                       0.264334   0.356112   0.742  0.45792
## Q98197.fctrYes                      0.528051   0.361524   1.461  0.14412
## Q98578.fctrNo                       0.402934   0.270069   1.492  0.13571
## Q98578.fctrYes                      0.489976   0.276778   1.770  0.07668
## Q98869.fctrNo                      -0.306007   0.276007  -1.109  0.26756
## Q98869.fctrYes                      0.082783   0.244636   0.338  0.73507
## Q99480.fctrNo                      -0.569692   0.361897  -1.574  0.11545
## Q99480.fctrYes                     -0.308076   0.345271  -0.892  0.37225
## Q99581.fctrNo                       0.506300   0.379873   1.333  0.18259
## Q99581.fctrYes                      0.514153   0.404803   1.270  0.20404
## Q99716.fctrNo                      -0.139306   0.321388  -0.433  0.66469
## Q99716.fctrYes                      0.046676   0.385354   0.121  0.90359
## `Q99982.fctrCheck!`                -0.103982   0.379256  -0.274  0.78395
## Q99982.fctrNope                    -0.146012   0.379269  -0.385  0.70025
## YOB.Age.fctr.L                     -0.143160   0.485123  -0.295  0.76792
## YOB.Age.fctr.Q                     -0.195212   0.413519  -0.472  0.63687
## YOB.Age.fctr.C                      0.713208   0.405735   1.758  0.07878
## `YOB.Age.fctr^4`                    0.220896   0.437639   0.505  0.61374
## `YOB.Age.fctr^5`                    0.424424   0.428171   0.991  0.32156
## `YOB.Age.fctr^6`                   -0.708352   0.366381  -1.933  0.05319
## `YOB.Age.fctr^7`                   -0.011800   0.348400  -0.034  0.97298
## `YOB.Age.fctr^8`                    0.635501   0.357048   1.780  0.07510
## `Q109244.fctrNA:.clusterid.fctr1`         NA         NA      NA       NA
## `Q109244.fctrNo:.clusterid.fctr1`   0.079753   0.312113   0.256  0.79832
## `Q109244.fctrYes:.clusterid.fctr1`        NA         NA      NA       NA
## `Q109244.fctrNA:.clusterid.fctr2`         NA         NA      NA       NA
## `Q109244.fctrNo:.clusterid.fctr2`  -0.165621   0.197001  -0.841  0.40051
## `Q109244.fctrYes:.clusterid.fctr2`        NA         NA      NA       NA
## `Q109244.fctrNA:.clusterid.fctr3`         NA         NA      NA       NA
## `Q109244.fctrNo:.clusterid.fctr3`         NA         NA      NA       NA
## `Q109244.fctrYes:.clusterid.fctr3`        NA         NA      NA       NA
## `YOB.Age.fctrNA:YOB.Age.dff`              NA         NA      NA       NA
## `YOB.Age.fctr(15,20]:YOB.Age.dff`  -0.077511   0.138920  -0.558  0.57688
## `YOB.Age.fctr(20,25]:YOB.Age.dff`  -0.058832   0.112939  -0.521  0.60242
## `YOB.Age.fctr(25,30]:YOB.Age.dff`   0.091120   0.111387   0.818  0.41333
## `YOB.Age.fctr(30,35]:YOB.Age.dff`  -0.210284   0.105677  -1.990  0.04660
## `YOB.Age.fctr(35,40]:YOB.Age.dff`  -0.038405   0.115339  -0.333  0.73915
## `YOB.Age.fctr(40,50]:YOB.Age.dff`   0.040738   0.048050   0.848  0.39654
## `YOB.Age.fctr(50,65]:YOB.Age.dff`   0.006217   0.036588   0.170  0.86507
## `YOB.Age.fctr(65,90]:YOB.Age.dff`  -0.053749   0.056666  -0.949  0.34286
##                                      
## (Intercept)                          
## .rnorm                               
## Edn.fctr.L                           
## Edn.fctr.Q                           
## Edn.fctr.C                           
## `Edn.fctr^4`                         
## `Edn.fctr^5`                         
## `Edn.fctr^6`                         
## `Edn.fctr^7`                         
## Gender.fctrF                         
## Gender.fctrM                         
## Hhold.fctrMKn                        
## Hhold.fctrMKy                        
## Hhold.fctrPKn                      **
## Hhold.fctrPKy                      * 
## Hhold.fctrSKn                        
## Hhold.fctrSKy                        
## Income.fctr.L                        
## Income.fctr.Q                        
## Income.fctr.C                      **
## `Income.fctr^4`                      
## `Income.fctr^5`                      
## `Income.fctr^6`                    * 
## Q100010.fctrNo                       
## Q100010.fctrYes                      
## Q100562.fctrNo                       
## Q100562.fctrYes                      
## Q100680.fctrNo                       
## Q100680.fctrYes                      
## Q100689.fctrNo                       
## Q100689.fctrYes                      
## Q101162.fctrOptimist               * 
## Q101162.fctrPessimist              * 
## Q101163.fctrDad                      
## Q101163.fctrMom                      
## Q101596.fctrNo                       
## Q101596.fctrYes                      
## Q102089.fctrOwn                      
## Q102089.fctrRent                     
## Q102289.fctrNo                       
## Q102289.fctrYes                      
## Q102674.fctrNo                       
## Q102674.fctrYes                    . 
## Q102687.fctrNo                       
## Q102687.fctrYes                      
## Q102906.fctrNo                       
## Q102906.fctrYes                      
## Q103293.fctrNo                       
## Q103293.fctrYes                      
## Q104996.fctrNo                       
## Q104996.fctrYes                      
## Q105655.fctrNo                       
## Q105655.fctrYes                      
## Q105840.fctrNo                       
## Q105840.fctrYes                      
## Q106042.fctrNo                       
## Q106042.fctrYes                      
## Q106272.fctrNo                       
## Q106272.fctrYes                      
## Q106388.fctrNo                       
## Q106388.fctrYes                      
## Q106389.fctrNo                       
## Q106389.fctrYes                      
## Q106993.fctrNo                       
## Q106993.fctrYes                      
## Q106997.fctrGr                       
## Q106997.fctrYy                       
## Q107491.fctrNo                       
## Q107491.fctrYes                      
## Q107869.fctrNo                       
## Q107869.fctrYes                      
## `Q108342.fctrIn-person`              
## Q108342.fctrOnline                 . 
## Q108343.fctrNo                       
## Q108343.fctrYes                      
## Q108617.fctrNo                     . 
## Q108617.fctrYes                      
## Q108754.fctrNo                       
## Q108754.fctrYes                      
## Q108855.fctrUmm...                   
## `Q108855.fctrYes!`                   
## Q108856.fctrSocialize                
## Q108856.fctrSpace                    
## Q108950.fctrCautious                 
## `Q108950.fctrRisk-friendly`          
## Q109367.fctrNo                       
## Q109367.fctrYes                      
## Q110740.fctrMac                      
## Q110740.fctrPC                       
## Q111220.fctrNo                       
## Q111220.fctrYes                      
## Q111580.fctrDemanding                
## Q111580.fctrSupportive               
## Q111848.fctrNo                       
## Q111848.fctrYes                      
## Q112270.fctrNo                       
## Q112270.fctrYes                      
## Q112478.fctrNo                     * 
## Q112478.fctrYes                    . 
## Q112512.fctrNo                     . 
## Q112512.fctrYes                    . 
## Q113181.fctrNo                       
## Q113181.fctrYes                      
## Q113583.fctrTalk                     
## Q113583.fctrTunes                    
## Q113584.fctrPeople                   
## Q113584.fctrTechnology               
## Q113992.fctrNo                       
## Q113992.fctrYes                      
## Q114152.fctrNo                       
## Q114152.fctrYes                      
## Q114386.fctrMysterious               
## Q114386.fctrTMI                      
## Q114517.fctrNo                     * 
## Q114517.fctrYes                    . 
## Q114748.fctrNo                       
## Q114748.fctrYes                      
## Q114961.fctrNo                       
## Q114961.fctrYes                      
## Q115195.fctrNo                       
## Q115195.fctrYes                    . 
## Q115390.fctrNo                       
## Q115390.fctrYes                      
## Q115602.fctrNo                       
## Q115602.fctrYes                      
## Q115610.fctrNo                       
## Q115610.fctrYes                      
## Q115611.fctrNo                       
## Q115611.fctrYes                      
## Q115777.fctrEnd                      
## Q115777.fctrStart                    
## Q115899.fctrCs                       
## Q115899.fctrMe                       
## Q116197.fctrA.M.                     
## Q116197.fctrP.M.                     
## Q116441.fctrNo                       
## Q116441.fctrYes                      
## Q116448.fctrNo                       
## Q116448.fctrYes                      
## Q116601.fctrNo                       
## Q116601.fctrYes                      
## Q116797.fctrNo                       
## Q116797.fctrYes                      
## Q116881.fctrHappy                  . 
## Q116881.fctrRight                    
## Q116953.fctrNo                     . 
## Q116953.fctrYes                      
## `Q117186.fctrCool headed`            
## `Q117186.fctrHot headed`             
## `Q117193.fctrOdd hours`              
## `Q117193.fctrStandard hours`         
## Q118117.fctrNo                       
## Q118117.fctrYes                      
## Q118232.fctrId                       
## Q118232.fctrPr                       
## Q118233.fctrNo                     . 
## Q118233.fctrYes                    * 
## Q118237.fctrNo                     * 
## Q118237.fctrYes                    * 
## Q118892.fctrNo                       
## Q118892.fctrYes                      
## Q119334.fctrNo                       
## Q119334.fctrYes                      
## Q119650.fctrGiving                   
## Q119650.fctrReceiving                
## Q119851.fctrNo                       
## Q119851.fctrYes                      
## Q120012.fctrNo                       
## Q120012.fctrYes                      
## Q120014.fctrNo                       
## Q120014.fctrYes                      
## `Q120194.fctrStudy first`            
## `Q120194.fctrTry first`              
## Q120379.fctrNo                       
## Q120379.fctrYes                      
## Q120472.fctrArt                      
## Q120472.fctrScience                  
## Q120650.fctrNo                       
## Q120650.fctrYes                      
## Q120978.fctrNo                       
## Q120978.fctrYes                      
## Q121011.fctrNo                       
## Q121011.fctrYes                      
## Q121699.fctrNo                     . 
## Q121699.fctrYes                    * 
## Q121700.fctrNo                     * 
## Q121700.fctrYes                    . 
## Q122120.fctrNo                       
## Q122120.fctrYes                      
## Q122769.fctrNo                       
## Q122769.fctrYes                      
## Q122770.fctrNo                       
## Q122770.fctrYes                      
## Q122771.fctrPc                     * 
## Q122771.fctrPt                     * 
## Q123464.fctrNo                       
## Q123464.fctrYes                      
## Q123621.fctrNo                       
## Q123621.fctrYes                      
## Q124122.fctrNo                       
## Q124122.fctrYes                    . 
## Q124742.fctrNo                       
## Q124742.fctrYes                      
## Q96024.fctrNo                        
## Q96024.fctrYes                       
## `Q98059.fctrOnly-child`              
## Q98059.fctrYes                       
## Q98078.fctrNo                        
## Q98078.fctrYes                       
## Q98197.fctrNo                        
## Q98197.fctrYes                       
## Q98578.fctrNo                        
## Q98578.fctrYes                     . 
## Q98869.fctrNo                        
## Q98869.fctrYes                       
## Q99480.fctrNo                        
## Q99480.fctrYes                       
## Q99581.fctrNo                        
## Q99581.fctrYes                       
## Q99716.fctrNo                        
## Q99716.fctrYes                       
## `Q99982.fctrCheck!`                  
## Q99982.fctrNope                      
## YOB.Age.fctr.L                       
## YOB.Age.fctr.Q                       
## YOB.Age.fctr.C                     . 
## `YOB.Age.fctr^4`                     
## `YOB.Age.fctr^5`                     
## `YOB.Age.fctr^6`                   . 
## `YOB.Age.fctr^7`                     
## `YOB.Age.fctr^8`                   . 
## `Q109244.fctrNA:.clusterid.fctr1`    
## `Q109244.fctrNo:.clusterid.fctr1`    
## `Q109244.fctrYes:.clusterid.fctr1`   
## `Q109244.fctrNA:.clusterid.fctr2`    
## `Q109244.fctrNo:.clusterid.fctr2`    
## `Q109244.fctrYes:.clusterid.fctr2`   
## `Q109244.fctrNA:.clusterid.fctr3`    
## `Q109244.fctrNo:.clusterid.fctr3`    
## `Q109244.fctrYes:.clusterid.fctr3`   
## `YOB.Age.fctrNA:YOB.Age.dff`         
## `YOB.Age.fctr(15,20]:YOB.Age.dff`    
## `YOB.Age.fctr(20,25]:YOB.Age.dff`    
## `YOB.Age.fctr(25,30]:YOB.Age.dff`    
## `YOB.Age.fctr(30,35]:YOB.Age.dff`  * 
## `YOB.Age.fctr(35,40]:YOB.Age.dff`    
## `YOB.Age.fctr(40,50]:YOB.Age.dff`    
## `YOB.Age.fctr(50,65]:YOB.Age.dff`    
## `YOB.Age.fctr(65,90]:YOB.Age.dff`    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2677.6  on 1965  degrees of freedom
## Residual deviance: 2315.4  on 1725  degrees of freedom
## AIC: 2797.4
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "mydisplayOutliers: "
## 
## No Studentized residuals with Bonferonni p < 0.05
## Largest |rstudent|:
##       rstudent unadjusted p-value Bonferonni p
## 3434 -2.477046           0.013248           NA
## [1] ""
##    .rstudent           .dffits           .hatvalues     
##  Min.   :-2.47705   Min.   :-1.07014   Min.   :0.01836  
##  1st Qu.:-1.11072   1st Qu.:-0.36992   1st Qu.:0.08418  
##  Median : 0.58007   Median : 0.13647   Median :0.10762  
##  Mean   : 0.04208   Mean   : 0.01383   Mean   :0.12258  
##  3rd Qu.: 1.00095   3rd Qu.: 0.33209   3rd Qu.:0.14522  
##  Max.   : 2.25788   Max.   : 1.19158   Max.   :0.40892  
## [1] "myfit_mdl: train diagnostics complete: 8.720000 secs"
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##          Prediction
## Reference   D   R
##         D 441 389
##         R 244 892
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.780264e-01   3.241638e-01   6.568648e-01   6.986558e-01   5.778230e-01 
## AccuracyPValue  McnemarPValue 
##   4.415879e-20   1.043601e-08
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

##          Prediction
## Reference   D   R
##         D   7 201
##         R   4 281
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.841785e-01   2.249712e-02   5.392613e-01   6.280802e-01   5.780933e-01 
## AccuracyPValue  McnemarPValue 
##   4.107189e-01   1.177443e-42 
## [1] "myfit_mdl: predict complete: 18.633000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                      6.627                 0.552
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6582683    0.5313253    0.7852113       0.7330763
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.7381051        0.5445999
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6568648             0.6986558    0.05614901
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5297993    0.3894231    0.6701754       0.5436235
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.15       0.7327249        0.5841785
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5392613             0.6280802    0.02249712
##   max.AccuracySD.fit max.KappaSD.fit
## 1          0.0164593      0.03436128
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading

## [1] "myfit_mdl: exit: 18.953000 secs"
##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 401.955 420.939
## 5 fit.models_1_preProc          1          4     preProc 420.939      NA
##   elapsed
## 4  18.984
## 5      NA
##                                                              id
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## Low.cor.X##rcv#glmnet           Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X##rcv#glmnet               Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## All.X##rcv#glm                  Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Q115611.fctr,Q113181.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Q115611.fctr,Q113181.fctr,Q115611.fctr:Q113181.fctr
##                                 max.nTuningRuns min.elapsedtime.everything
## Low.cor.X##rcv#glmnet                        25                     14.978
## All.X##rcv#glmnet                            25                     15.017
## All.X##rcv#glm                                1                      6.627
## Max.cor.Y.rcv.1X1###glmnet                    0                      0.807
## Interact.High.cor.Y##rcv#glmnet              25                      2.243
##                                 min.elapsedtime.final max.AUCpROC.fit
## Low.cor.X##rcv#glmnet                           1.446       0.5828451
## All.X##rcv#glmnet                               1.449       0.5828451
## All.X##rcv#glm                                  0.552       0.6582683
## Max.cor.Y.rcv.1X1###glmnet                      0.029       0.5770809
## Interact.High.cor.Y##rcv#glmnet                 0.055       0.5981249
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## Low.cor.X##rcv#glmnet              0.3144578    0.8512324       0.6581803
## All.X##rcv#glmnet                  0.3144578    0.8512324       0.6581803
## All.X##rcv#glm                     0.5313253    0.7852113       0.7330763
## Max.cor.Y.rcv.1X1###glmnet         0.3566265    0.7975352       0.6231477
## Interact.High.cor.Y##rcv#glmnet    0.4638554    0.7323944       0.6231477
##                                 opt.prob.threshold.fit max.f.score.fit
## Low.cor.X##rcv#glmnet                             0.55       0.6902429
## All.X##rcv#glmnet                                 0.55       0.6902429
## All.X##rcv#glm                                    0.50       0.7381051
## Max.cor.Y.rcv.1X1###glmnet                        0.55       0.6895980
## Interact.High.cor.Y##rcv#glmnet                   0.50       0.6895980
##                                 max.Accuracy.fit max.AccuracyLower.fit
## Low.cor.X##rcv#glmnet                  0.6037574             0.6084387
## All.X##rcv#glmnet                      0.6037574             0.6084387
## All.X##rcv#glm                         0.5445999             0.6568648
## Max.cor.Y.rcv.1X1###glmnet             0.6190234             0.5971380
## Interact.High.cor.Y##rcv#glmnet        0.6168144             0.5971380
##                                 max.AccuracyUpper.fit max.Kappa.fit
## Low.cor.X##rcv#glmnet                       0.6515995    0.13070637
## All.X##rcv#glmnet                           0.6515995    0.13070637
## All.X##rcv#glm                              0.6986558    0.05614901
## Max.cor.Y.rcv.1X1###glmnet                  0.6405531    0.20084510
## Interact.High.cor.Y##rcv#glmnet             0.6405531    0.19381078
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## Low.cor.X##rcv#glmnet                 0.5370867    0.2355769    0.8385965
## All.X##rcv#glmnet                     0.5370867    0.2355769    0.8385965
## All.X##rcv#glm                        0.5297993    0.3894231    0.6701754
## Max.cor.Y.rcv.1X1###glmnet            0.5332490    0.2980769    0.7684211
## Interact.High.cor.Y##rcv#glmnet       0.5326586    0.3846154    0.6807018
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## Low.cor.X##rcv#glmnet                 0.5838394                   0.45
## All.X##rcv#glmnet                     0.5838394                   0.45
## All.X##rcv#glm                        0.5436235                   0.15
## Max.cor.Y.rcv.1X1###glmnet            0.5660003                   0.40
## Interact.High.cor.Y##rcv#glmnet       0.5660003                   0.40
##                                 max.f.score.OOB max.Accuracy.OOB
## Low.cor.X##rcv#glmnet                 0.7267833        0.5882353
## All.X##rcv#glmnet                     0.7267833        0.5882353
## All.X##rcv#glm                        0.7327249        0.5841785
## Max.cor.Y.rcv.1X1###glmnet            0.7326478        0.5780933
## Interact.High.cor.Y##rcv#glmnet       0.7326478        0.5780933
##                                 max.AccuracyLower.OOB
## Low.cor.X##rcv#glmnet                       0.5433559
## All.X##rcv#glmnet                           0.5433559
## All.X##rcv#glm                              0.5392613
## Max.cor.Y.rcv.1X1###glmnet                  0.5331249
## Interact.High.cor.Y##rcv#glmnet             0.5331249
##                                 max.AccuracyUpper.OOB max.Kappa.OOB
## Low.cor.X##rcv#glmnet                       0.6320501    0.04903125
## All.X##rcv#glmnet                           0.6320501    0.04903125
## All.X##rcv#glm                              0.6280802    0.02249712
## Max.cor.Y.rcv.1X1###glmnet                  0.6221196    0.00000000
## Interact.High.cor.Y##rcv#glmnet             0.6221196    0.00000000
##                                 max.AccuracySD.fit max.KappaSD.fit
## Low.cor.X##rcv#glmnet                   0.01089705      0.02153295
## All.X##rcv#glmnet                       0.01089705      0.02153295
## All.X##rcv#glm                          0.01645930      0.03436128
## Max.cor.Y.rcv.1X1###glmnet                      NA              NA
## Interact.High.cor.Y##rcv#glmnet         0.01509263      0.03210463
##                                 min.elapsedtime.everything
## Random###myrandom_classfr                            0.397
## MFO###myMFO_classfr                                  0.580
## Max.cor.Y.rcv.1X1###glmnet                           0.807
## Max.cor.Y##rcv#rpart                                 1.549
## Interact.High.cor.Y##rcv#glmnet                      2.243
## All.X##rcv#glm                                       6.627
## Low.cor.X##rcv#glmnet                               14.978
## All.X##rcv#glmnet                                   15.017
##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 420.939 421.016
## 6     fit.models_1_end          1          5    teardown 421.016      NA
##   elapsed
## 5   0.077
## 6      NA
##         label step_major step_minor label_minor     bgn     end elapsed
## 17 fit.models          8          1           1 370.651 421.026  50.375
## 18 fit.models          8          2           2 421.026      NA      NA

```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 423.366  NA      NA
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
## Warning: Removed 4 rows containing missing values (geom_errorbar).
## quartz_off_screen 
##                 2
## Warning: Removed 4 rows containing missing values (geom_errorbar).

##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 6           Low.cor.X##rcv#glmnet        0.5882353       0.5838394
## 7               All.X##rcv#glmnet        0.5882353       0.5838394
## 8                  All.X##rcv#glm        0.5841785       0.5436235
## 3      Max.cor.Y.rcv.1X1###glmnet        0.5780933       0.5660003
## 5 Interact.High.cor.Y##rcv#glmnet        0.5780933       0.5660003
## 4            Max.cor.Y##rcv#rpart        0.5780933       0.5440283
## 2       Random###myrandom_classfr        0.5780933       0.5142038
## 1             MFO###myMFO_classfr        0.5780933       0.5000000
##   max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 6       0.5370867                     14.978        0.6037574
## 7       0.5370867                     15.017        0.6037574
## 8       0.5297993                      6.627        0.5445999
## 3       0.5332490                      0.807        0.6190234
## 5       0.5326586                      2.243        0.6168144
## 4       0.5326586                      1.549        0.6174930
## 2       0.4988023                      0.397        0.5778230
## 1       0.5000000                      0.580        0.5778230
##   opt.prob.threshold.fit opt.prob.threshold.OOB
## 6                   0.55                   0.45
## 7                   0.55                   0.45
## 8                   0.50                   0.15
## 3                   0.55                   0.40
## 5                   0.50                   0.40
## 4                   0.50                   0.40
## 2                   0.40                   0.40
## 1                   0.40                   0.40
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything - 
##     max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7f9d39351a70>
## [1] "Best model id: Low.cor.X##rcv#glmnet"
## [1] "User specified selection: All.X##rcv#glmnet"
## glmnet 
## 
## 1966 samples
##  109 predictor
##    2 classes: 'D', 'R' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times) 
## Summary of sample sizes: 1312, 1310, 1310, 1311, 1310, 1311, ... 
## Resampling results across tuning parameters:
## 
##   alpha  lambda        Accuracy   Kappa     
##   0.100  7.416788e-05  0.5474840  0.06197487
##   0.100  3.442568e-04  0.5490089  0.06467607
##   0.100  1.597898e-03  0.5508731  0.06647164
##   0.100  7.416788e-03  0.5625743  0.08698028
##   0.100  3.442568e-02  0.5851208  0.12212690
##   0.325  7.416788e-05  0.5479929  0.06296942
##   0.325  3.442568e-04  0.5490091  0.06417522
##   0.325  1.597898e-03  0.5518909  0.06828571
##   0.325  7.416788e-03  0.5691867  0.09707201
##   0.325  3.442568e-02  0.5973221  0.12972600
##   0.550  7.416788e-05  0.5471450  0.06126057
##   0.550  3.442568e-04  0.5486688  0.06326212
##   0.550  1.597898e-03  0.5571480  0.07825962
##   0.550  7.416788e-03  0.5763070  0.10839764
##   0.550  3.442568e-02  0.6037574  0.13070637
##   0.775  7.416788e-05  0.5466361  0.06014652
##   0.775  3.442568e-04  0.5483301  0.06236174
##   0.775  1.597898e-03  0.5593512  0.08239904
##   0.775  7.416788e-03  0.5829179  0.11697026
##   0.775  3.442568e-02  0.6010474  0.11261152
##   1.000  7.416788e-05  0.5462968  0.05933642
##   1.000  3.442568e-04  0.5486683  0.06265923
##   1.000  1.597898e-03  0.5632500  0.08857402
##   1.000  7.416788e-03  0.5902107  0.12691893
##   1.000  3.442568e-02  0.5895150  0.06872935
## 
## Accuracy was used to select the optimal model using  the largest value.
## The final values used for the model were alpha = 0.55 and lambda
##  = 0.03442568.
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
##                                  All.X..rcv.glmnet.imp         imp
## Hhold.fctrPKn                              100.0000000 100.0000000
## Q115611.fctrNo                              74.8310371  74.8310371
## Q113181.fctrNo                              71.9217479  71.9217479
## Q115611.fctrYes                             70.4290930  70.4290930
## Q98869.fctrNo                               66.6128974  66.6128974
## Q101163.fctrDad                             62.4655160  62.4655160
## Q113181.fctrYes                             55.5170996  55.5170996
## Q116881.fctrRight                           49.4278917  49.4278917
## Q99480.fctrNo                               43.2534158  43.2534158
## Q109244.fctrNo:.clusterid.fctr2             39.5589149  39.5589149
## Q98197.fctrYes                              30.2889588  30.2889588
## Q120472.fctrScience                         28.6966467  28.6966467
## Hhold.fctrPKy                               27.2269256  27.2269256
## Q108855.fctrYes!                            20.8545071  20.8545071
## Q106272.fctrNo                              20.6694877  20.6694877
## Q110740.fctrPC                              20.2380396  20.2380396
## Q98197.fctrNo                               16.4908495  16.4908495
## YOB.Age.fctr^6                              15.7172616  15.7172616
## Hhold.fctrMKy                               15.1524950  15.1524950
## Q116953.fctrNo                              14.7571499  14.7571499
## Q115899.fctrCs                               8.2575355   8.2575355
## Q116881.fctrHappy                            6.1528740   6.1528740
## Q120379.fctrNo                               4.1519631   4.1519631
## Q106997.fctrYy                               4.0717791   4.0717791
## Q115195.fctrYes                              0.7006634   0.7006634
## Q120379.fctrYes                              0.0718631   0.0718631
## .rnorm                                       0.0000000   0.0000000
## Edn.fctr.L                                   0.0000000   0.0000000
## Edn.fctr.Q                                   0.0000000   0.0000000
## Edn.fctr.C                                   0.0000000   0.0000000
## Edn.fctr^4                                   0.0000000   0.0000000
## Edn.fctr^5                                   0.0000000   0.0000000
## Edn.fctr^6                                   0.0000000   0.0000000
## Edn.fctr^7                                   0.0000000   0.0000000
## Gender.fctrF                                 0.0000000   0.0000000
## Gender.fctrM                                 0.0000000   0.0000000
## Hhold.fctrMKn                                0.0000000   0.0000000
## Hhold.fctrSKn                                0.0000000   0.0000000
## Hhold.fctrSKy                                0.0000000   0.0000000
## Income.fctr.L                                0.0000000   0.0000000
## Income.fctr.Q                                0.0000000   0.0000000
## Income.fctr.C                                0.0000000   0.0000000
## Income.fctr^4                                0.0000000   0.0000000
## Income.fctr^5                                0.0000000   0.0000000
## Income.fctr^6                                0.0000000   0.0000000
## Q100010.fctrNo                               0.0000000   0.0000000
## Q100010.fctrYes                              0.0000000   0.0000000
## Q100562.fctrNo                               0.0000000   0.0000000
## Q100562.fctrYes                              0.0000000   0.0000000
## Q100680.fctrNo                               0.0000000   0.0000000
## Q100680.fctrYes                              0.0000000   0.0000000
## Q100689.fctrNo                               0.0000000   0.0000000
## Q100689.fctrYes                              0.0000000   0.0000000
## Q101162.fctrOptimist                         0.0000000   0.0000000
## Q101162.fctrPessimist                        0.0000000   0.0000000
## Q101163.fctrMom                              0.0000000   0.0000000
## Q101596.fctrNo                               0.0000000   0.0000000
## Q101596.fctrYes                              0.0000000   0.0000000
## Q102089.fctrOwn                              0.0000000   0.0000000
## Q102089.fctrRent                             0.0000000   0.0000000
## Q102289.fctrNo                               0.0000000   0.0000000
## Q102289.fctrYes                              0.0000000   0.0000000
## Q102674.fctrNo                               0.0000000   0.0000000
## Q102674.fctrYes                              0.0000000   0.0000000
## Q102687.fctrNo                               0.0000000   0.0000000
## Q102687.fctrYes                              0.0000000   0.0000000
## Q102906.fctrNo                               0.0000000   0.0000000
## Q102906.fctrYes                              0.0000000   0.0000000
## Q103293.fctrNo                               0.0000000   0.0000000
## Q103293.fctrYes                              0.0000000   0.0000000
## Q104996.fctrNo                               0.0000000   0.0000000
## Q104996.fctrYes                              0.0000000   0.0000000
## Q105655.fctrNo                               0.0000000   0.0000000
## Q105655.fctrYes                              0.0000000   0.0000000
## Q105840.fctrNo                               0.0000000   0.0000000
## Q105840.fctrYes                              0.0000000   0.0000000
## Q106042.fctrNo                               0.0000000   0.0000000
## Q106042.fctrYes                              0.0000000   0.0000000
## Q106272.fctrYes                              0.0000000   0.0000000
## Q106388.fctrNo                               0.0000000   0.0000000
## Q106388.fctrYes                              0.0000000   0.0000000
## Q106389.fctrNo                               0.0000000   0.0000000
## Q106389.fctrYes                              0.0000000   0.0000000
## Q106993.fctrNo                               0.0000000   0.0000000
## Q106993.fctrYes                              0.0000000   0.0000000
## Q106997.fctrGr                               0.0000000   0.0000000
## Q107491.fctrNo                               0.0000000   0.0000000
## Q107491.fctrYes                              0.0000000   0.0000000
## Q107869.fctrNo                               0.0000000   0.0000000
## Q107869.fctrYes                              0.0000000   0.0000000
## Q108342.fctrIn-person                        0.0000000   0.0000000
## Q108342.fctrOnline                           0.0000000   0.0000000
## Q108343.fctrNo                               0.0000000   0.0000000
## Q108343.fctrYes                              0.0000000   0.0000000
## Q108617.fctrNo                               0.0000000   0.0000000
## Q108617.fctrYes                              0.0000000   0.0000000
## Q108754.fctrNo                               0.0000000   0.0000000
## Q108754.fctrYes                              0.0000000   0.0000000
## Q108855.fctrUmm...                           0.0000000   0.0000000
## Q108856.fctrSocialize                        0.0000000   0.0000000
## Q108856.fctrSpace                            0.0000000   0.0000000
## Q108950.fctrCautious                         0.0000000   0.0000000
## Q108950.fctrRisk-friendly                    0.0000000   0.0000000
## Q109367.fctrNo                               0.0000000   0.0000000
## Q109367.fctrYes                              0.0000000   0.0000000
## Q110740.fctrMac                              0.0000000   0.0000000
## Q111220.fctrNo                               0.0000000   0.0000000
## Q111220.fctrYes                              0.0000000   0.0000000
## Q111580.fctrDemanding                        0.0000000   0.0000000
## Q111580.fctrSupportive                       0.0000000   0.0000000
## Q111848.fctrNo                               0.0000000   0.0000000
## Q111848.fctrYes                              0.0000000   0.0000000
## Q112270.fctrNo                               0.0000000   0.0000000
## Q112270.fctrYes                              0.0000000   0.0000000
## Q112478.fctrNo                               0.0000000   0.0000000
## Q112478.fctrYes                              0.0000000   0.0000000
## Q112512.fctrNo                               0.0000000   0.0000000
## Q112512.fctrYes                              0.0000000   0.0000000
## Q113583.fctrTalk                             0.0000000   0.0000000
## Q113583.fctrTunes                            0.0000000   0.0000000
## Q113584.fctrPeople                           0.0000000   0.0000000
## Q113584.fctrTechnology                       0.0000000   0.0000000
## Q113992.fctrNo                               0.0000000   0.0000000
## Q113992.fctrYes                              0.0000000   0.0000000
## Q114152.fctrNo                               0.0000000   0.0000000
## Q114152.fctrYes                              0.0000000   0.0000000
## Q114386.fctrMysterious                       0.0000000   0.0000000
## Q114386.fctrTMI                              0.0000000   0.0000000
## Q114517.fctrNo                               0.0000000   0.0000000
## Q114517.fctrYes                              0.0000000   0.0000000
## Q114748.fctrNo                               0.0000000   0.0000000
## Q114748.fctrYes                              0.0000000   0.0000000
## Q114961.fctrNo                               0.0000000   0.0000000
## Q114961.fctrYes                              0.0000000   0.0000000
## Q115195.fctrNo                               0.0000000   0.0000000
## Q115390.fctrNo                               0.0000000   0.0000000
## Q115390.fctrYes                              0.0000000   0.0000000
## Q115602.fctrNo                               0.0000000   0.0000000
## Q115602.fctrYes                              0.0000000   0.0000000
## Q115610.fctrNo                               0.0000000   0.0000000
## Q115610.fctrYes                              0.0000000   0.0000000
## Q115777.fctrEnd                              0.0000000   0.0000000
## Q115777.fctrStart                            0.0000000   0.0000000
## Q115899.fctrMe                               0.0000000   0.0000000
## Q116197.fctrA.M.                             0.0000000   0.0000000
## Q116197.fctrP.M.                             0.0000000   0.0000000
## Q116441.fctrNo                               0.0000000   0.0000000
## Q116441.fctrYes                              0.0000000   0.0000000
## Q116448.fctrNo                               0.0000000   0.0000000
## Q116448.fctrYes                              0.0000000   0.0000000
## Q116601.fctrNo                               0.0000000   0.0000000
## Q116601.fctrYes                              0.0000000   0.0000000
## Q116797.fctrNo                               0.0000000   0.0000000
## Q116797.fctrYes                              0.0000000   0.0000000
## Q116953.fctrYes                              0.0000000   0.0000000
## Q117186.fctrCool headed                      0.0000000   0.0000000
## Q117186.fctrHot headed                       0.0000000   0.0000000
## Q117193.fctrOdd hours                        0.0000000   0.0000000
## Q117193.fctrStandard hours                   0.0000000   0.0000000
## Q118117.fctrNo                               0.0000000   0.0000000
## Q118117.fctrYes                              0.0000000   0.0000000
## Q118232.fctrId                               0.0000000   0.0000000
## Q118232.fctrPr                               0.0000000   0.0000000
## Q118233.fctrNo                               0.0000000   0.0000000
## Q118233.fctrYes                              0.0000000   0.0000000
## Q118237.fctrNo                               0.0000000   0.0000000
## Q118237.fctrYes                              0.0000000   0.0000000
## Q118892.fctrNo                               0.0000000   0.0000000
## Q118892.fctrYes                              0.0000000   0.0000000
## Q119334.fctrNo                               0.0000000   0.0000000
## Q119334.fctrYes                              0.0000000   0.0000000
## Q119650.fctrGiving                           0.0000000   0.0000000
## Q119650.fctrReceiving                        0.0000000   0.0000000
## Q119851.fctrNo                               0.0000000   0.0000000
## Q119851.fctrYes                              0.0000000   0.0000000
## Q120012.fctrNo                               0.0000000   0.0000000
## Q120012.fctrYes                              0.0000000   0.0000000
## Q120014.fctrNo                               0.0000000   0.0000000
## Q120014.fctrYes                              0.0000000   0.0000000
## Q120194.fctrStudy first                      0.0000000   0.0000000
## Q120194.fctrTry first                        0.0000000   0.0000000
## Q120472.fctrArt                              0.0000000   0.0000000
## Q120650.fctrNo                               0.0000000   0.0000000
## Q120650.fctrYes                              0.0000000   0.0000000
## Q120978.fctrNo                               0.0000000   0.0000000
## Q120978.fctrYes                              0.0000000   0.0000000
## Q121011.fctrNo                               0.0000000   0.0000000
## Q121011.fctrYes                              0.0000000   0.0000000
## Q121699.fctrNo                               0.0000000   0.0000000
## Q121699.fctrYes                              0.0000000   0.0000000
## Q121700.fctrNo                               0.0000000   0.0000000
## Q121700.fctrYes                              0.0000000   0.0000000
## Q122120.fctrNo                               0.0000000   0.0000000
## Q122120.fctrYes                              0.0000000   0.0000000
## Q122769.fctrNo                               0.0000000   0.0000000
## Q122769.fctrYes                              0.0000000   0.0000000
## Q122770.fctrNo                               0.0000000   0.0000000
## Q122770.fctrYes                              0.0000000   0.0000000
## Q122771.fctrPc                               0.0000000   0.0000000
## Q122771.fctrPt                               0.0000000   0.0000000
## Q123464.fctrNo                               0.0000000   0.0000000
## Q123464.fctrYes                              0.0000000   0.0000000
## Q123621.fctrNo                               0.0000000   0.0000000
## Q123621.fctrYes                              0.0000000   0.0000000
## Q124122.fctrNo                               0.0000000   0.0000000
## Q124122.fctrYes                              0.0000000   0.0000000
## Q124742.fctrNo                               0.0000000   0.0000000
## Q124742.fctrYes                              0.0000000   0.0000000
## Q96024.fctrNo                                0.0000000   0.0000000
## Q96024.fctrYes                               0.0000000   0.0000000
## Q98059.fctrOnly-child                        0.0000000   0.0000000
## Q98059.fctrYes                               0.0000000   0.0000000
## Q98078.fctrNo                                0.0000000   0.0000000
## Q98078.fctrYes                               0.0000000   0.0000000
## Q98578.fctrNo                                0.0000000   0.0000000
## Q98578.fctrYes                               0.0000000   0.0000000
## Q98869.fctrYes                               0.0000000   0.0000000
## Q99480.fctrYes                               0.0000000   0.0000000
## Q99581.fctrNo                                0.0000000   0.0000000
## Q99581.fctrYes                               0.0000000   0.0000000
## Q99716.fctrNo                                0.0000000   0.0000000
## Q99716.fctrYes                               0.0000000   0.0000000
## Q99982.fctrCheck!                            0.0000000   0.0000000
## Q99982.fctrNope                              0.0000000   0.0000000
## YOB.Age.fctr.L                               0.0000000   0.0000000
## YOB.Age.fctr.Q                               0.0000000   0.0000000
## YOB.Age.fctr.C                               0.0000000   0.0000000
## YOB.Age.fctr^4                               0.0000000   0.0000000
## YOB.Age.fctr^5                               0.0000000   0.0000000
## YOB.Age.fctr^7                               0.0000000   0.0000000
## YOB.Age.fctr^8                               0.0000000   0.0000000
## Q109244.fctrNA:.clusterid.fctr1              0.0000000   0.0000000
## Q109244.fctrNo:.clusterid.fctr1              0.0000000   0.0000000
## Q109244.fctrYes:.clusterid.fctr1             0.0000000   0.0000000
## Q109244.fctrNA:.clusterid.fctr2              0.0000000   0.0000000
## Q109244.fctrYes:.clusterid.fctr2             0.0000000   0.0000000
## Q109244.fctrNA:.clusterid.fctr3              0.0000000   0.0000000
## Q109244.fctrNo:.clusterid.fctr3              0.0000000   0.0000000
## Q109244.fctrYes:.clusterid.fctr3             0.0000000   0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                   0.0000000   0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff              0.0000000   0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff              0.0000000   0.0000000
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1     470          R                         0.3781065
## 2    2226          R                         0.3976514
## 3     493          R                         0.4089913
## 4    2051          R                         0.4100291
## 5    4042          R                         0.4157935
## 6    3082          R                         0.4183314
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.6218935                               FALSE
## 2                            0.6023486                               FALSE
## 3                            0.5910087                               FALSE
## 4                            0.5899709                               FALSE
## 5                            0.5842065                               FALSE
## 6                            0.5816686                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                        -0.07189353
## 2                                 FALSE                        -0.05234860
## 3                                 FALSE                        -0.04100873
## 4                                 FALSE                        -0.03997085
## 5                                 FALSE                        -0.03420648
## 6                                 FALSE                        -0.03166862
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 5      4042          R                         0.4157935
## 14       32          R                         0.4417232
## 114    4156          D                         0.5770600
## 118    4084          D                         0.5835408
## 128    1140          D                         0.5906073
## 188    2369          D                         0.6854429
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 5                              D                             TRUE
## 14                             D                             TRUE
## 114                            R                             TRUE
## 118                            R                             TRUE
## 128                            R                             TRUE
## 188                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 5                              0.5842065
## 14                             0.5582768
## 114                            0.5770600
## 118                            0.5835408
## 128                            0.5906073
## 188                            0.6854429
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 5                                 FALSE
## 14                                FALSE
## 114                               FALSE
## 118                               FALSE
## 128                               FALSE
## 188                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 5                                   FALSE
## 14                                  FALSE
## 114                                 FALSE
## 118                                 FALSE
## 128                                 FALSE
## 188                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 5                         -0.034206477
## 14                        -0.008276834
## 114                        0.127059950
## 118                        0.133540782
## 128                        0.140607326
## 188                        0.235442940
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 198     853          D                         0.7160185
## 199    3625          D                         0.7183170
## 200    1339          D                         0.7222690
## 201     217          D                         0.7229525
## 202    1992          D                         0.7236406
## 203     613          D                         0.7283414
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 198                            R                             TRUE
## 199                            R                             TRUE
## 200                            R                             TRUE
## 201                            R                             TRUE
## 202                            R                             TRUE
## 203                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 198                            0.7160185
## 199                            0.7183170
## 200                            0.7222690
## 201                            0.7229525
## 202                            0.7236406
## 203                            0.7283414
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 198                               FALSE
## 199                               FALSE
## 200                               FALSE
## 201                               FALSE
## 202                               FALSE
## 203                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 198                                 FALSE
## 199                                 FALSE
## 200                                 FALSE
## 201                                 FALSE
## 202                                 FALSE
## 203                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 198                          0.2660185
## 199                          0.2683170
## 200                          0.2722690
## 201                          0.2729525
## 202                          0.2736406
## 203                          0.2783414

##    Q109244.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## No           No    493   1966    622              1              1
##    .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## No              1        914.5756        0.4651961   1966        234.7403
##    err.abs.OOB.mean
## No        0.4761466
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##      493.0000000     1966.0000000      622.0000000        1.0000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##        1.0000000        1.0000000      914.5755991        0.4651961 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##     1966.0000000      234.7402522        0.4761466
##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 430.22  NA      NA
##         label step_major step_minor label_minor     bgn     end elapsed
## 18 fit.models          8          2           2 421.026 430.229   9.203
## 19 fit.models          8          3           3 430.230      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 19        fit.models          8          3           3 430.230 433.296
## 20 fit.data.training          9          0           0 433.297      NA
##    elapsed
## 19   3.067
## 20      NA

Step 9.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var]))) {    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
    if (myparseMdlId(glbMdlSelId)$family == "RFE.X") {
        indepVar <- mygetIndepVar(glb_feats_df)
        trnRFEResults <- 
            myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
        if (!isTRUE(all.equal(sort(predictors(trnRFEResults)),
                              sort(predictors(glbRFEResults))))) {
            print("Diffs predictors(trnRFEResults) vs. predictors(glbRFEResults):")
            print(setdiff(predictors(trnRFEResults), predictors(glbRFEResults)))
            print("Diffs predictors(glbRFEResults) vs. predictors(trnRFEResults):")
            print(setdiff(predictors(glbRFEResults), predictors(trnRFEResults)))
        }
    }

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        mdlIndepVar <- row.names(mdlimp_df)
        if (glb_is_classification)
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlIndepVar)] else
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlIndepVar)]
        # Fit selected models on glbObsTrn
        for (mdl_id in mdlIdVcr) {
            mdl_id_components <- myparseMdlId(mdl_id)
            mdlIdPfx <- mdl_id_components$family
            # if (grepl("RFE\\.X\\.", mdlIdPfx)) 
            #     mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
            #         predictors(trnRFEResults))) else
                # mdlIndepVars <- trim(unlist(
                #     strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            thsIndepVar <- unlist(
                    strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]"))
            thsSpc <- myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = paste0("Final.", mdlIdPfx), 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = mdl_id_components$resample,
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = mdl_id_components$alg,
                        train.preProcess = mdl_id_components$preProcess))
            ret_lst <- myfit_mdl(mdl_specs_lst = thsSpc,
                    indepVar = thsIndepVar,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = thsSpc$id, 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = thsSpc$id, 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        mdlIndepVar <- row.names(mdlimp_df)        
        if (glb_is_classification)
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlIndepVar)] else
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlIndepVar)]
        mdlIdVcr <- paste("Final", mdlIdVcr, sep = ".")
        mdlIndepVar <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"), mdlIndepVar, fixed = TRUE)
        
        # if (glb_is_classification && glb_is_binomial)
        #     indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
        #                             row.names(mdlimp_df)) else
        #     indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
        #                             row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        # indepVar <- myextract_actual_feats(predictors(trnRFEResults))
        mdlIndepVar <- myextract_actual_feats(predictors(glbRFEResults))        
    } else mdlIndepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    # if (!is.null(glbMdlPreprocMethods) &&
    #     ((match_pos <- regexpr(gsub(".", "\\.", 
    #                                 paste(glbMdlPreprocMethods, collapse = "|"),
    #                                fixed = TRUE), glbMdlSelId)) != -1))
    #     ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
    #                             match_pos + attr(match_pos, "match.length") - 1) else
    #     ths_preProcess <- NULL   
    
    # mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
    #                                "Final.Ensemble", "Final")
    thsMdlId <- paste0("Final.", glbMdlSelId)
    thsMdlIdComponents <- myparseMdlId(thsMdlId)
    # mdl_id_pfx <- paste("Final", myparseMdlId(glbMdlSelId)$family, sep = ".")
    mdl_id_pfx <- thsMdlIdComponents$family
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    # method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))

    thsSpc <- myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = mdl_id_pfx, 
        type = glb_model_type, tune.df = glbMdlTuneParams,
        trainControl.method = thsMdlIdComponents$resample,
        trainControl.number = glb_rcv_n_folds,
        trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = thsMdlIdComponents$alg,
        train.preProcess = thsMdlIdComponents$preProcess))

    glbMdlFinId <- thsSpc$id
    if (!(grepl("Ensemble", glbMdlSelId)))
        ret_lst <- myfit_mdl(mdl_specs_lst = thsSpc,
                             indepVar = mdlIndepVar,
                             rsp_var = glb_rsp_var, 
                             fit_df = glbObsTrn, OOB_df = NULL) else {
                                 
        # Final model same as selected model except for the model features
        tmp_models_df <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
        tmp_models_df$id <- paste0("Final.", tmp_models_df$id)
        row.names(tmp_models_df) <- tmp_models_df$id
        tmp_models_df$feats <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
                                    tmp_models_df$feats, fixed = TRUE)
        glb_models_df <- rbind(glb_models_df, tmp_models_df)
        
        tmp_fin_mdl <- glb_sel_mdl
        # tmp_fin_mdl$coefnames <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #                               tmp_fin_mdl$coefnames, fixed = TRUE)
        # dimnames(tmp_fin_mdl$finalModel$beta)[[1]] <- 
        #     gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #         dimnames(tmp_fin_mdl$finalModel$beta)[[1]], fixed = TRUE)
        # tmp_fin_mdl$finalModel$xNames <- 
        #     gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #         tmp_fin_mdl$finalModel$xNames, fixed = TRUE)
        # 
        # thsAts <- attributes(tmp_fin_mdl$terms)
        # # thsAts$variables <- class == "call" & objects / symbols are stored as a formula
        # thsAts$term.labels <- 
        #     gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #         thsAts$term.labels, fixed = TRUE)
        # attributes(tmp_fin_mdl$terms) <- thsAts
        # 
        glb_models_lst[[glbMdlFinId]] <- tmp_fin_mdl
    }
    
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]] 
}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.684000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0316 on full training set
## [1] "myfit_mdl: train complete: 18.633000 secs"
## Warning in myfit_mdl(mdl_specs_lst = thsSpc, indepVar = mdlIndepVar,
## rsp_var = glb_rsp_var, : model's bestTune found at an extreme of tuneGrid
## for parameter: lambda

##             Length Class      Mode     
## a0             75  -none-     numeric  
## beta        18600  dgCMatrix  S4       
## df             75  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         75  -none-     numeric  
## dev.ratio      75  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        248  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                    0.2970169682                    0.0513665757 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                   -0.2838557632                   -0.0008998049 
##                   Income.fctr.Q                  Q100562.fctrNo 
##                    0.0291344589                   -0.0080125635 
##                 Q101163.fctrDad                  Q106272.fctrNo 
##                    0.1186987147                   -0.0270178645 
##                  Q106997.fctrGr                Q108855.fctrYes! 
##                    0.0517027973                    0.0392115902 
##                  Q110740.fctrPC                  Q113181.fctrNo 
##                    0.0627772273                   -0.1712567515 
##                 Q113181.fctrYes                  Q115611.fctrNo 
##                    0.0673416516                   -0.1001127511 
##                 Q115611.fctrYes               Q116881.fctrRight 
##                    0.2275131539                    0.1642609924 
##                   Q98197.fctrNo                   Q98869.fctrNo 
##                   -0.1712736620                   -0.1059987327 
##                   Q99480.fctrNo Q109244.fctrNo:.clusterid.fctr2 
##                   -0.0661417738                   -0.0723266563 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                     0.288708416                     0.060678421 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                    -0.318837645                    -0.067999748 
##                   Income.fctr.Q                   Income.fctr^6 
##                     0.047659160                    -0.004784633 
##                  Q100562.fctrNo                 Q101163.fctrDad 
##                    -0.028077170                     0.129505996 
##                  Q106272.fctrNo                  Q106997.fctrGr 
##                    -0.038375513                     0.071350406 
##                Q108855.fctrYes!                  Q110740.fctrPC 
##                     0.051707657                     0.075505834 
##                  Q113181.fctrNo                 Q113181.fctrYes 
##                    -0.178368468                     0.066189401 
##                  Q115611.fctrNo                 Q115611.fctrYes 
##                    -0.106296095                     0.229654922 
##                  Q115899.fctrCs               Q116881.fctrHappy 
##                    -0.009884619                    -0.003625296 
##               Q116881.fctrRight                  Q116953.fctrNo 
##                     0.174080150                     0.007503033 
##                  Q118232.fctrId             Q120472.fctrScience 
##                    -0.004865184                     0.010983305 
##                  Q122120.fctrNo                   Q98197.fctrNo 
##                    -0.005886664                    -0.177014715 
##                   Q98869.fctrNo                   Q99480.fctrNo 
##                    -0.120044949                    -0.077426499 
## Q109244.fctrNo:.clusterid.fctr2 YOB.Age.fctr(35,40]:YOB.Age.dff 
##                    -0.073202658                    -0.004835400 
## [1] "myfit_mdl: train diagnostics complete: 19.306000 secs"

##          Prediction
## Reference    D    R
##         D  485  553
##         R  379 1042
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.209841e-01   2.051659e-01   6.014708e-01   6.402089e-01   5.778772e-01 
## AccuracyPValue  McnemarPValue 
##   7.486567e-06   1.454861e-08 
## [1] "myfit_mdl: predict complete: 25.729000 secs"
##                        id
## 1 Final.All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,YOB.Age.fctr:YOB.Age.dff,Q109244.fctr:.clusterid.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     17.861                 1.628
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5729879    0.2909441    0.8550317       0.6490527
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.55       0.6909814         0.597397
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6014708             0.6402089     0.1121178
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01441578      0.02911097
## [1] "myfit_mdl: exit: 25.752000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn     end
## 20 fit.data.training          9          0           0 433.297 459.565
## 21 fit.data.training          9          1           1 459.566      NA
##    elapsed
## 20  26.268
## 21      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification)
    #     mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    # mdlEnsembleComps <- gsub(paste0("^", 
    #                     gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
    #                          "", mdlEnsembleComps)
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlEnsembleComps)] else
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$value  %in% mdlEnsembleComps)]
                        
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        # glb_fin_mdl uses the same coefficients as glb_sel_mdl, 
        #   so copy the "Final" columns into "non-Final" columns
        glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
        glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.45
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                                  All.X..rcv.glmnet.imp
## Hhold.fctrPKn                              100.0000000
## Q115611.fctrYes                             70.4290930
## Q113181.fctrNo                              71.9217479
## Q98197.fctrNo                               16.4908495
## Q116881.fctrRight                           49.4278917
## Q101163.fctrDad                             62.4655160
## Q98869.fctrNo                               66.6128974
## Q115611.fctrNo                              74.8310371
## Q109244.fctrNo:.clusterid.fctr2             39.5589149
## Q99480.fctrNo                               43.2534158
## Q110740.fctrPC                              20.2380396
## Q113181.fctrYes                             55.5170996
## Q106997.fctrGr                               0.0000000
## Hhold.fctrMKy                               15.1524950
## Q108855.fctrYes!                            20.8545071
## Income.fctr.Q                                0.0000000
## Hhold.fctrPKy                               27.2269256
## Q106272.fctrNo                              20.6694877
## Q100562.fctrNo                               0.0000000
## Q120472.fctrScience                         28.6966467
## Q115899.fctrCs                               8.2575355
## Q116953.fctrNo                              14.7571499
## Q122120.fctrNo                               0.0000000
## Q118232.fctrId                               0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff              0.0000000
## Income.fctr^6                                0.0000000
## Q116881.fctrHappy                            6.1528740
## .rnorm                                       0.0000000
## Edn.fctr.C                                   0.0000000
## Edn.fctr.L                                   0.0000000
## Edn.fctr.Q                                   0.0000000
## Edn.fctr^4                                   0.0000000
## Edn.fctr^5                                   0.0000000
## Edn.fctr^6                                   0.0000000
## Edn.fctr^7                                   0.0000000
## Gender.fctrF                                 0.0000000
## Gender.fctrM                                 0.0000000
## Hhold.fctrMKn                                0.0000000
## Hhold.fctrSKn                                0.0000000
## Hhold.fctrSKy                                0.0000000
## Income.fctr.C                                0.0000000
## Income.fctr.L                                0.0000000
## Income.fctr^4                                0.0000000
## Income.fctr^5                                0.0000000
## Q100010.fctrNo                               0.0000000
## Q100010.fctrYes                              0.0000000
## Q100562.fctrYes                              0.0000000
## Q100680.fctrNo                               0.0000000
## Q100680.fctrYes                              0.0000000
## Q100689.fctrNo                               0.0000000
## Q100689.fctrYes                              0.0000000
## Q101162.fctrOptimist                         0.0000000
## Q101162.fctrPessimist                        0.0000000
## Q101163.fctrMom                              0.0000000
## Q101596.fctrNo                               0.0000000
## Q101596.fctrYes                              0.0000000
## Q102089.fctrOwn                              0.0000000
## Q102089.fctrRent                             0.0000000
## Q102289.fctrNo                               0.0000000
## Q102289.fctrYes                              0.0000000
## Q102674.fctrNo                               0.0000000
## Q102674.fctrYes                              0.0000000
## Q102687.fctrNo                               0.0000000
## Q102687.fctrYes                              0.0000000
## Q102906.fctrNo                               0.0000000
## Q102906.fctrYes                              0.0000000
## Q103293.fctrNo                               0.0000000
## Q103293.fctrYes                              0.0000000
## Q104996.fctrNo                               0.0000000
## Q104996.fctrYes                              0.0000000
## Q105655.fctrNo                               0.0000000
## Q105655.fctrYes                              0.0000000
## Q105840.fctrNo                               0.0000000
## Q105840.fctrYes                              0.0000000
## Q106042.fctrNo                               0.0000000
## Q106042.fctrYes                              0.0000000
## Q106272.fctrYes                              0.0000000
## Q106388.fctrNo                               0.0000000
## Q106388.fctrYes                              0.0000000
## Q106389.fctrNo                               0.0000000
## Q106389.fctrYes                              0.0000000
## Q106993.fctrNo                               0.0000000
## Q106993.fctrYes                              0.0000000
## Q106997.fctrYy                               4.0717791
## Q107491.fctrNo                               0.0000000
## Q107491.fctrYes                              0.0000000
## Q107869.fctrNo                               0.0000000
## Q107869.fctrYes                              0.0000000
## Q108342.fctrIn-person                        0.0000000
## Q108342.fctrOnline                           0.0000000
## Q108343.fctrNo                               0.0000000
## Q108343.fctrYes                              0.0000000
## Q108617.fctrNo                               0.0000000
## Q108617.fctrYes                              0.0000000
## Q108754.fctrNo                               0.0000000
## Q108754.fctrYes                              0.0000000
## Q108855.fctrUmm...                           0.0000000
## Q108856.fctrSocialize                        0.0000000
## Q108856.fctrSpace                            0.0000000
## Q108950.fctrCautious                         0.0000000
## Q108950.fctrRisk-friendly                    0.0000000
## Q109244.fctrNA:.clusterid.fctr1              0.0000000
## Q109244.fctrNA:.clusterid.fctr2              0.0000000
## Q109244.fctrNA:.clusterid.fctr3              0.0000000
## Q109244.fctrNo:.clusterid.fctr1              0.0000000
## Q109244.fctrNo:.clusterid.fctr3              0.0000000
## Q109244.fctrYes:.clusterid.fctr1             0.0000000
## Q109244.fctrYes:.clusterid.fctr2             0.0000000
## Q109244.fctrYes:.clusterid.fctr3             0.0000000
## Q109367.fctrNo                               0.0000000
## Q109367.fctrYes                              0.0000000
## Q110740.fctrMac                              0.0000000
## Q111220.fctrNo                               0.0000000
## Q111220.fctrYes                              0.0000000
## Q111580.fctrDemanding                        0.0000000
## Q111580.fctrSupportive                       0.0000000
## Q111848.fctrNo                               0.0000000
## Q111848.fctrYes                              0.0000000
## Q112270.fctrNo                               0.0000000
## Q112270.fctrYes                              0.0000000
## Q112478.fctrNo                               0.0000000
## Q112478.fctrYes                              0.0000000
## Q112512.fctrNo                               0.0000000
## Q112512.fctrYes                              0.0000000
## Q113583.fctrTalk                             0.0000000
## Q113583.fctrTunes                            0.0000000
## Q113584.fctrPeople                           0.0000000
## Q113584.fctrTechnology                       0.0000000
## Q113992.fctrNo                               0.0000000
## Q113992.fctrYes                              0.0000000
## Q114152.fctrNo                               0.0000000
## Q114152.fctrYes                              0.0000000
## Q114386.fctrMysterious                       0.0000000
## Q114386.fctrTMI                              0.0000000
## Q114517.fctrNo                               0.0000000
## Q114517.fctrYes                              0.0000000
## Q114748.fctrNo                               0.0000000
## Q114748.fctrYes                              0.0000000
## Q114961.fctrNo                               0.0000000
## Q114961.fctrYes                              0.0000000
## Q115195.fctrNo                               0.0000000
## Q115195.fctrYes                              0.7006634
## Q115390.fctrNo                               0.0000000
## Q115390.fctrYes                              0.0000000
## Q115602.fctrNo                               0.0000000
## Q115602.fctrYes                              0.0000000
## Q115610.fctrNo                               0.0000000
## Q115610.fctrYes                              0.0000000
## Q115777.fctrEnd                              0.0000000
## Q115777.fctrStart                            0.0000000
## Q115899.fctrMe                               0.0000000
## Q116197.fctrA.M.                             0.0000000
## Q116197.fctrP.M.                             0.0000000
## Q116441.fctrNo                               0.0000000
## Q116441.fctrYes                              0.0000000
## Q116448.fctrNo                               0.0000000
## Q116448.fctrYes                              0.0000000
## Q116601.fctrNo                               0.0000000
## Q116601.fctrYes                              0.0000000
## Q116797.fctrNo                               0.0000000
## Q116797.fctrYes                              0.0000000
## Q116953.fctrYes                              0.0000000
## Q117186.fctrCool headed                      0.0000000
## Q117186.fctrHot headed                       0.0000000
## Q117193.fctrOdd hours                        0.0000000
## Q117193.fctrStandard hours                   0.0000000
## Q118117.fctrNo                               0.0000000
## Q118117.fctrYes                              0.0000000
## Q118232.fctrPr                               0.0000000
## Q118233.fctrNo                               0.0000000
## Q118233.fctrYes                              0.0000000
## Q118237.fctrNo                               0.0000000
## Q118237.fctrYes                              0.0000000
## Q118892.fctrNo                               0.0000000
## Q118892.fctrYes                              0.0000000
## Q119334.fctrNo                               0.0000000
## Q119334.fctrYes                              0.0000000
## Q119650.fctrGiving                           0.0000000
## Q119650.fctrReceiving                        0.0000000
## Q119851.fctrNo                               0.0000000
## Q119851.fctrYes                              0.0000000
## Q120012.fctrNo                               0.0000000
## Q120012.fctrYes                              0.0000000
## Q120014.fctrNo                               0.0000000
## Q120014.fctrYes                              0.0000000
## Q120194.fctrStudy first                      0.0000000
## Q120194.fctrTry first                        0.0000000
## Q120379.fctrNo                               4.1519631
## Q120379.fctrYes                              0.0718631
## Q120472.fctrArt                              0.0000000
## Q120650.fctrNo                               0.0000000
## Q120650.fctrYes                              0.0000000
## Q120978.fctrNo                               0.0000000
## Q120978.fctrYes                              0.0000000
## Q121011.fctrNo                               0.0000000
## Q121011.fctrYes                              0.0000000
## Q121699.fctrNo                               0.0000000
## Q121699.fctrYes                              0.0000000
## Q121700.fctrNo                               0.0000000
## Q121700.fctrYes                              0.0000000
## Q122120.fctrYes                              0.0000000
## Q122769.fctrNo                               0.0000000
## Q122769.fctrYes                              0.0000000
## Q122770.fctrNo                               0.0000000
## Q122770.fctrYes                              0.0000000
## Q122771.fctrPc                               0.0000000
## Q122771.fctrPt                               0.0000000
## Q123464.fctrNo                               0.0000000
## Q123464.fctrYes                              0.0000000
## Q123621.fctrNo                               0.0000000
## Q123621.fctrYes                              0.0000000
## Q124122.fctrNo                               0.0000000
## Q124122.fctrYes                              0.0000000
## Q124742.fctrNo                               0.0000000
## Q124742.fctrYes                              0.0000000
## Q96024.fctrNo                                0.0000000
## Q96024.fctrYes                               0.0000000
## Q98059.fctrOnly-child                        0.0000000
## Q98059.fctrYes                               0.0000000
## Q98078.fctrNo                                0.0000000
## Q98078.fctrYes                               0.0000000
## Q98197.fctrYes                              30.2889588
## Q98578.fctrNo                                0.0000000
## Q98578.fctrYes                               0.0000000
## Q98869.fctrYes                               0.0000000
## Q99480.fctrYes                               0.0000000
## Q99581.fctrNo                                0.0000000
## Q99581.fctrYes                               0.0000000
## Q99716.fctrNo                                0.0000000
## Q99716.fctrYes                               0.0000000
## Q99982.fctrCheck!                            0.0000000
## Q99982.fctrNope                              0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff              0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff              0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff              0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff              0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff              0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff              0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff              0.0000000
## YOB.Age.fctr.C                               0.0000000
## YOB.Age.fctr.L                               0.0000000
## YOB.Age.fctr.Q                               0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                   0.0000000
## YOB.Age.fctr^4                               0.0000000
## YOB.Age.fctr^5                               0.0000000
## YOB.Age.fctr^6                              15.7172616
## YOB.Age.fctr^7                               0.0000000
## YOB.Age.fctr^8                               0.0000000
##                                  Final.All.X..rcv.glmnet.imp         imp
## Hhold.fctrPKn                                    100.0000000 100.0000000
## Q115611.fctrYes                                   75.9584309  75.9584309
## Q113181.fctrNo                                    58.0668000  58.0668000
## Q98197.fctrNo                                     57.8505154  57.8505154
## Q116881.fctrRight                                 56.1801537  56.1801537
## Q101163.fctrDad                                   41.1979661  41.1979661
## Q98869.fctrNo                                     37.5016257  37.5016257
## Q115611.fctrNo                                    34.2725138  34.2725138
## Q109244.fctrNo:.clusterid.fctr2                   24.1788511  24.1788511
## Q99480.fctrNo                                     23.8084963  23.8084963
## Q110740.fctrPC                                    22.9240816  22.9240816
## Q113181.fctrYes                                   22.1937700  22.1937700
## Q106997.fctrGr                                    20.3637641  20.3637641
## Hhold.fctrMKy                                     18.5787083  18.5787083
## Q108855.fctrYes!                                  15.0546341  15.0546341
## Income.fctr.Q                                     12.6816120  12.6816120
## Hhold.fctrPKy                                     11.1622362  11.1622362
## Q106272.fctrNo                                    10.8178681  10.8178681
## Q100562.fctrNo                                     5.9112686   5.9112686
## Q120472.fctrScience                                1.7781504   1.7781504
## Q115899.fctrCs                                     1.6002778   1.6002778
## Q116953.fctrNo                                     1.2147091   1.2147091
## Q122120.fctrNo                                     0.9530258   0.9530258
## Q118232.fctrId                                     0.7876526   0.7876526
## YOB.Age.fctr(35,40]:YOB.Age.dff                    0.7828307   0.7828307
## Income.fctr^6                                      0.7746117   0.7746117
## Q116881.fctrHappy                                  0.5869200   0.5869200
## .rnorm                                             0.0000000   0.0000000
## Edn.fctr.C                                         0.0000000   0.0000000
## Edn.fctr.L                                         0.0000000   0.0000000
## Edn.fctr.Q                                         0.0000000   0.0000000
## Edn.fctr^4                                         0.0000000   0.0000000
## Edn.fctr^5                                         0.0000000   0.0000000
## Edn.fctr^6                                         0.0000000   0.0000000
## Edn.fctr^7                                         0.0000000   0.0000000
## Gender.fctrF                                       0.0000000   0.0000000
## Gender.fctrM                                       0.0000000   0.0000000
## Hhold.fctrMKn                                      0.0000000   0.0000000
## Hhold.fctrSKn                                      0.0000000   0.0000000
## Hhold.fctrSKy                                      0.0000000   0.0000000
## Income.fctr.C                                      0.0000000   0.0000000
## Income.fctr.L                                      0.0000000   0.0000000
## Income.fctr^4                                      0.0000000   0.0000000
## Income.fctr^5                                      0.0000000   0.0000000
## Q100010.fctrNo                                     0.0000000   0.0000000
## Q100010.fctrYes                                    0.0000000   0.0000000
## Q100562.fctrYes                                    0.0000000   0.0000000
## Q100680.fctrNo                                     0.0000000   0.0000000
## Q100680.fctrYes                                    0.0000000   0.0000000
## Q100689.fctrNo                                     0.0000000   0.0000000
## Q100689.fctrYes                                    0.0000000   0.0000000
## Q101162.fctrOptimist                               0.0000000   0.0000000
## Q101162.fctrPessimist                              0.0000000   0.0000000
## Q101163.fctrMom                                    0.0000000   0.0000000
## Q101596.fctrNo                                     0.0000000   0.0000000
## Q101596.fctrYes                                    0.0000000   0.0000000
## Q102089.fctrOwn                                    0.0000000   0.0000000
## Q102089.fctrRent                                   0.0000000   0.0000000
## Q102289.fctrNo                                     0.0000000   0.0000000
## Q102289.fctrYes                                    0.0000000   0.0000000
## Q102674.fctrNo                                     0.0000000   0.0000000
## Q102674.fctrYes                                    0.0000000   0.0000000
## Q102687.fctrNo                                     0.0000000   0.0000000
## Q102687.fctrYes                                    0.0000000   0.0000000
## Q102906.fctrNo                                     0.0000000   0.0000000
## Q102906.fctrYes                                    0.0000000   0.0000000
## Q103293.fctrNo                                     0.0000000   0.0000000
## Q103293.fctrYes                                    0.0000000   0.0000000
## Q104996.fctrNo                                     0.0000000   0.0000000
## Q104996.fctrYes                                    0.0000000   0.0000000
## Q105655.fctrNo                                     0.0000000   0.0000000
## Q105655.fctrYes                                    0.0000000   0.0000000
## Q105840.fctrNo                                     0.0000000   0.0000000
## Q105840.fctrYes                                    0.0000000   0.0000000
## Q106042.fctrNo                                     0.0000000   0.0000000
## Q106042.fctrYes                                    0.0000000   0.0000000
## Q106272.fctrYes                                    0.0000000   0.0000000
## Q106388.fctrNo                                     0.0000000   0.0000000
## Q106388.fctrYes                                    0.0000000   0.0000000
## Q106389.fctrNo                                     0.0000000   0.0000000
## Q106389.fctrYes                                    0.0000000   0.0000000
## Q106993.fctrNo                                     0.0000000   0.0000000
## Q106993.fctrYes                                    0.0000000   0.0000000
## Q106997.fctrYy                                     0.0000000   0.0000000
## Q107491.fctrNo                                     0.0000000   0.0000000
## Q107491.fctrYes                                    0.0000000   0.0000000
## Q107869.fctrNo                                     0.0000000   0.0000000
## Q107869.fctrYes                                    0.0000000   0.0000000
## Q108342.fctrIn-person                              0.0000000   0.0000000
## Q108342.fctrOnline                                 0.0000000   0.0000000
## Q108343.fctrNo                                     0.0000000   0.0000000
## Q108343.fctrYes                                    0.0000000   0.0000000
## Q108617.fctrNo                                     0.0000000   0.0000000
## Q108617.fctrYes                                    0.0000000   0.0000000
## Q108754.fctrNo                                     0.0000000   0.0000000
## Q108754.fctrYes                                    0.0000000   0.0000000
## Q108855.fctrUmm...                                 0.0000000   0.0000000
## Q108856.fctrSocialize                              0.0000000   0.0000000
## Q108856.fctrSpace                                  0.0000000   0.0000000
## Q108950.fctrCautious                               0.0000000   0.0000000
## Q108950.fctrRisk-friendly                          0.0000000   0.0000000
## Q109244.fctrNA:.clusterid.fctr1                    0.0000000   0.0000000
## Q109244.fctrNA:.clusterid.fctr2                    0.0000000   0.0000000
## Q109244.fctrNA:.clusterid.fctr3                    0.0000000   0.0000000
## Q109244.fctrNo:.clusterid.fctr1                    0.0000000   0.0000000
## Q109244.fctrNo:.clusterid.fctr3                    0.0000000   0.0000000
## Q109244.fctrYes:.clusterid.fctr1                   0.0000000   0.0000000
## Q109244.fctrYes:.clusterid.fctr2                   0.0000000   0.0000000
## Q109244.fctrYes:.clusterid.fctr3                   0.0000000   0.0000000
## Q109367.fctrNo                                     0.0000000   0.0000000
## Q109367.fctrYes                                    0.0000000   0.0000000
## Q110740.fctrMac                                    0.0000000   0.0000000
## Q111220.fctrNo                                     0.0000000   0.0000000
## Q111220.fctrYes                                    0.0000000   0.0000000
## Q111580.fctrDemanding                              0.0000000   0.0000000
## Q111580.fctrSupportive                             0.0000000   0.0000000
## Q111848.fctrNo                                     0.0000000   0.0000000
## Q111848.fctrYes                                    0.0000000   0.0000000
## Q112270.fctrNo                                     0.0000000   0.0000000
## Q112270.fctrYes                                    0.0000000   0.0000000
## Q112478.fctrNo                                     0.0000000   0.0000000
## Q112478.fctrYes                                    0.0000000   0.0000000
## Q112512.fctrNo                                     0.0000000   0.0000000
## Q112512.fctrYes                                    0.0000000   0.0000000
## Q113583.fctrTalk                                   0.0000000   0.0000000
## Q113583.fctrTunes                                  0.0000000   0.0000000
## Q113584.fctrPeople                                 0.0000000   0.0000000
## Q113584.fctrTechnology                             0.0000000   0.0000000
## Q113992.fctrNo                                     0.0000000   0.0000000
## Q113992.fctrYes                                    0.0000000   0.0000000
## Q114152.fctrNo                                     0.0000000   0.0000000
## Q114152.fctrYes                                    0.0000000   0.0000000
## Q114386.fctrMysterious                             0.0000000   0.0000000
## Q114386.fctrTMI                                    0.0000000   0.0000000
## Q114517.fctrNo                                     0.0000000   0.0000000
## Q114517.fctrYes                                    0.0000000   0.0000000
## Q114748.fctrNo                                     0.0000000   0.0000000
## Q114748.fctrYes                                    0.0000000   0.0000000
## Q114961.fctrNo                                     0.0000000   0.0000000
## Q114961.fctrYes                                    0.0000000   0.0000000
## Q115195.fctrNo                                     0.0000000   0.0000000
## Q115195.fctrYes                                    0.0000000   0.0000000
## Q115390.fctrNo                                     0.0000000   0.0000000
## Q115390.fctrYes                                    0.0000000   0.0000000
## Q115602.fctrNo                                     0.0000000   0.0000000
## Q115602.fctrYes                                    0.0000000   0.0000000
## Q115610.fctrNo                                     0.0000000   0.0000000
## Q115610.fctrYes                                    0.0000000   0.0000000
## Q115777.fctrEnd                                    0.0000000   0.0000000
## Q115777.fctrStart                                  0.0000000   0.0000000
## Q115899.fctrMe                                     0.0000000   0.0000000
## Q116197.fctrA.M.                                   0.0000000   0.0000000
## Q116197.fctrP.M.                                   0.0000000   0.0000000
## Q116441.fctrNo                                     0.0000000   0.0000000
## Q116441.fctrYes                                    0.0000000   0.0000000
## Q116448.fctrNo                                     0.0000000   0.0000000
## Q116448.fctrYes                                    0.0000000   0.0000000
## Q116601.fctrNo                                     0.0000000   0.0000000
## Q116601.fctrYes                                    0.0000000   0.0000000
## Q116797.fctrNo                                     0.0000000   0.0000000
## Q116797.fctrYes                                    0.0000000   0.0000000
## Q116953.fctrYes                                    0.0000000   0.0000000
## Q117186.fctrCool headed                            0.0000000   0.0000000
## Q117186.fctrHot headed                             0.0000000   0.0000000
## Q117193.fctrOdd hours                              0.0000000   0.0000000
## Q117193.fctrStandard hours                         0.0000000   0.0000000
## Q118117.fctrNo                                     0.0000000   0.0000000
## Q118117.fctrYes                                    0.0000000   0.0000000
## Q118232.fctrPr                                     0.0000000   0.0000000
## Q118233.fctrNo                                     0.0000000   0.0000000
## Q118233.fctrYes                                    0.0000000   0.0000000
## Q118237.fctrNo                                     0.0000000   0.0000000
## Q118237.fctrYes                                    0.0000000   0.0000000
## Q118892.fctrNo                                     0.0000000   0.0000000
## Q118892.fctrYes                                    0.0000000   0.0000000
## Q119334.fctrNo                                     0.0000000   0.0000000
## Q119334.fctrYes                                    0.0000000   0.0000000
## Q119650.fctrGiving                                 0.0000000   0.0000000
## Q119650.fctrReceiving                              0.0000000   0.0000000
## Q119851.fctrNo                                     0.0000000   0.0000000
## Q119851.fctrYes                                    0.0000000   0.0000000
## Q120012.fctrNo                                     0.0000000   0.0000000
## Q120012.fctrYes                                    0.0000000   0.0000000
## Q120014.fctrNo                                     0.0000000   0.0000000
## Q120014.fctrYes                                    0.0000000   0.0000000
## Q120194.fctrStudy first                            0.0000000   0.0000000
## Q120194.fctrTry first                              0.0000000   0.0000000
## Q120379.fctrNo                                     0.0000000   0.0000000
## Q120379.fctrYes                                    0.0000000   0.0000000
## Q120472.fctrArt                                    0.0000000   0.0000000
## Q120650.fctrNo                                     0.0000000   0.0000000
## Q120650.fctrYes                                    0.0000000   0.0000000
## Q120978.fctrNo                                     0.0000000   0.0000000
## Q120978.fctrYes                                    0.0000000   0.0000000
## Q121011.fctrNo                                     0.0000000   0.0000000
## Q121011.fctrYes                                    0.0000000   0.0000000
## Q121699.fctrNo                                     0.0000000   0.0000000
## Q121699.fctrYes                                    0.0000000   0.0000000
## Q121700.fctrNo                                     0.0000000   0.0000000
## Q121700.fctrYes                                    0.0000000   0.0000000
## Q122120.fctrYes                                    0.0000000   0.0000000
## Q122769.fctrNo                                     0.0000000   0.0000000
## Q122769.fctrYes                                    0.0000000   0.0000000
## Q122770.fctrNo                                     0.0000000   0.0000000
## Q122770.fctrYes                                    0.0000000   0.0000000
## Q122771.fctrPc                                     0.0000000   0.0000000
## Q122771.fctrPt                                     0.0000000   0.0000000
## Q123464.fctrNo                                     0.0000000   0.0000000
## Q123464.fctrYes                                    0.0000000   0.0000000
## Q123621.fctrNo                                     0.0000000   0.0000000
## Q123621.fctrYes                                    0.0000000   0.0000000
## Q124122.fctrNo                                     0.0000000   0.0000000
## Q124122.fctrYes                                    0.0000000   0.0000000
## Q124742.fctrNo                                     0.0000000   0.0000000
## Q124742.fctrYes                                    0.0000000   0.0000000
## Q96024.fctrNo                                      0.0000000   0.0000000
## Q96024.fctrYes                                     0.0000000   0.0000000
## Q98059.fctrOnly-child                              0.0000000   0.0000000
## Q98059.fctrYes                                     0.0000000   0.0000000
## Q98078.fctrNo                                      0.0000000   0.0000000
## Q98078.fctrYes                                     0.0000000   0.0000000
## Q98197.fctrYes                                     0.0000000   0.0000000
## Q98578.fctrNo                                      0.0000000   0.0000000
## Q98578.fctrYes                                     0.0000000   0.0000000
## Q98869.fctrYes                                     0.0000000   0.0000000
## Q99480.fctrYes                                     0.0000000   0.0000000
## Q99581.fctrNo                                      0.0000000   0.0000000
## Q99581.fctrYes                                     0.0000000   0.0000000
## Q99716.fctrNo                                      0.0000000   0.0000000
## Q99716.fctrYes                                     0.0000000   0.0000000
## Q99982.fctrCheck!                                  0.0000000   0.0000000
## Q99982.fctrNope                                    0.0000000   0.0000000
## YOB.Age.fctr(15,20]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff                    0.0000000   0.0000000
## YOB.Age.fctr.C                                     0.0000000   0.0000000
## YOB.Age.fctr.L                                     0.0000000   0.0000000
## YOB.Age.fctr.Q                                     0.0000000   0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                         0.0000000   0.0000000
## YOB.Age.fctr^4                                     0.0000000   0.0000000
## YOB.Age.fctr^5                                     0.0000000   0.0000000
## YOB.Age.fctr^6                                     0.0000000   0.0000000
## YOB.Age.fctr^7                                     0.0000000   0.0000000
## YOB.Age.fctr^8                                     0.0000000   0.0000000
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    5879          R                         0.3726079
## 2      26          R                         0.3732665
## 3     599          R                         0.3873659
## 4     470          R                                NA
## 5     403          R                         0.3926555
## 6    1610          R                         0.4161595
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                         <NA>                               NA
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.6273921                               FALSE
## 2                            0.6267335                               FALSE
## 3                            0.6126341                               FALSE
## 4                                   NA                                  NA
## 5                            0.6073445                               FALSE
## 6                            0.5838405                               FALSE
##   Party.fctr.Final.All.X..rcv.glmnet.prob
## 1                               0.3676277
## 2                               0.3726328
## 3                               0.3915592
## 4                               0.3923804
## 5                               0.3977130
## 6                               0.3981271
##   Party.fctr.Final.All.X..rcv.glmnet
## 1                                  D
## 2                                  D
## 3                                  D
## 4                                  D
## 5                                  D
## 6                                  D
##   Party.fctr.Final.All.X..rcv.glmnet.err
## 1                                   TRUE
## 2                                   TRUE
## 3                                   TRUE
## 4                                   TRUE
## 5                                   TRUE
## 6                                   TRUE
##   Party.fctr.Final.All.X..rcv.glmnet.err.abs
## 1                                  0.6323723
## 2                                  0.6273672
## 3                                  0.6084408
## 4                                  0.6076196
## 5                                  0.6022870
## 6                                  0.6018729
##   Party.fctr.Final.All.X..rcv.glmnet.is.acc
## 1                                     FALSE
## 2                                     FALSE
## 3                                     FALSE
## 4                                     FALSE
## 5                                     FALSE
## 6                                     FALSE
##   Party.fctr.Final.All.X..rcv.glmnet.accurate
## 1                                       FALSE
## 2                                       FALSE
## 3                                       FALSE
## 4                                       FALSE
## 5                                       FALSE
## 6                                       FALSE
##   Party.fctr.Final.All.X..rcv.glmnet.error
## 1                              -0.08237233
## 2                              -0.07736717
## 3                              -0.05844080
## 4                              -0.05761956
## 5                              -0.05228705
## 6                              -0.05187289
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 22     2533          R                         0.4279397
## 41     4737          R                         0.4548967
## 482    5951          D                         0.5586695
## 512    2447          D                         0.5671780
## 578    5229          D                                NA
## 989     943          D                         0.7140905
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 22                             D                             TRUE
## 41                             R                            FALSE
## 482                            R                             TRUE
## 512                            R                             TRUE
## 578                         <NA>                               NA
## 989                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 22                             0.5720603
## 41                             0.5451033
## 482                            0.5586695
## 512                            0.5671780
## 578                                   NA
## 989                            0.7140905
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 22                                FALSE
## 41                                 TRUE
## 482                               FALSE
## 512                               FALSE
## 578                                  NA
## 989                               FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.prob
## 22                                0.4345395
## 41                                0.4474682
## 482                               0.5556392
## 512                               0.5612856
## 578                               0.5727992
## 989                               0.7151865
##     Party.fctr.Final.All.X..rcv.glmnet
## 22                                   D
## 41                                   D
## 482                                  R
## 512                                  R
## 578                                  R
## 989                                  R
##     Party.fctr.Final.All.X..rcv.glmnet.err
## 22                                    TRUE
## 41                                    TRUE
## 482                                   TRUE
## 512                                   TRUE
## 578                                   TRUE
## 989                                   TRUE
##     Party.fctr.Final.All.X..rcv.glmnet.err.abs
## 22                                   0.5654605
## 41                                   0.5525318
## 482                                  0.5556392
## 512                                  0.5612856
## 578                                  0.5727992
## 989                                  0.7151865
##     Party.fctr.Final.All.X..rcv.glmnet.is.acc
## 22                                      FALSE
## 41                                      FALSE
## 482                                     FALSE
## 512                                     FALSE
## 578                                     FALSE
## 989                                     FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.accurate
## 22                                        FALSE
## 41                                        FALSE
## 482                                       FALSE
## 512                                       FALSE
## 578                                       FALSE
## 989                                       FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.error
## 22                              -0.015460479
## 41                              -0.002531803
## 482                              0.105639209
## 512                              0.111285591
## 578                              0.122799196
## 989                              0.265186481
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1000    2641          D                         0.7109417
## 1001      78          D                         0.7237733
## 1002    4956          D                         0.7264982
## 1003    3474          D                         0.7430687
## 1004    1309          D                         0.7395013
## 1005    3578          D                         0.7389856
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1000                            R                             TRUE
## 1001                            R                             TRUE
## 1002                            R                             TRUE
## 1003                            R                             TRUE
## 1004                            R                             TRUE
## 1005                            R                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 1000                            0.7109417
## 1001                            0.7237733
## 1002                            0.7264982
## 1003                            0.7430687
## 1004                            0.7395013
## 1005                            0.7389856
##      Party.fctr.All.X..rcv.glmnet.is.acc
## 1000                               FALSE
## 1001                               FALSE
## 1002                               FALSE
## 1003                               FALSE
## 1004                               FALSE
## 1005                               FALSE
##      Party.fctr.Final.All.X..rcv.glmnet.prob
## 1000                               0.7258532
## 1001                               0.7347881
## 1002                               0.7348269
## 1003                               0.7375263
## 1004                               0.7446751
## 1005                               0.7482297
##      Party.fctr.Final.All.X..rcv.glmnet
## 1000                                  R
## 1001                                  R
## 1002                                  R
## 1003                                  R
## 1004                                  R
## 1005                                  R
##      Party.fctr.Final.All.X..rcv.glmnet.err
## 1000                                   TRUE
## 1001                                   TRUE
## 1002                                   TRUE
## 1003                                   TRUE
## 1004                                   TRUE
## 1005                                   TRUE
##      Party.fctr.Final.All.X..rcv.glmnet.err.abs
## 1000                                  0.7258532
## 1001                                  0.7347881
## 1002                                  0.7348269
## 1003                                  0.7375263
## 1004                                  0.7446751
## 1005                                  0.7482297
##      Party.fctr.Final.All.X..rcv.glmnet.is.acc
## 1000                                     FALSE
## 1001                                     FALSE
## 1002                                     FALSE
## 1003                                     FALSE
## 1004                                     FALSE
## 1005                                     FALSE
##      Party.fctr.Final.All.X..rcv.glmnet.accurate
## 1000                                       FALSE
## 1001                                       FALSE
## 1002                                       FALSE
## 1003                                       FALSE
## 1004                                       FALSE
## 1005                                       FALSE
##      Party.fctr.Final.All.X..rcv.glmnet.error
## 1000                                0.2758532
## 1001                                0.2847881
## 1002                                0.2848269
## 1003                                0.2875263
## 1004                                0.2946751
## 1005                                0.2982297

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final.All.X..rcv.glmnet.prob"   
## [2] "Party.fctr.Final.All.X..rcv.glmnet"        
## [3] "Party.fctr.Final.All.X..rcv.glmnet.err"    
## [4] "Party.fctr.Final.All.X..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final.All.X..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 21 fit.data.training          9          1           1 459.566 466.883
## 22  predict.data.new         10          0           0 466.884      NA
##    elapsed
## 21   7.318
## 22      NA

Step 10.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.45
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## NULL
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] "Stacking file Votes_Ensemble_cnk06_out_fin.csv to prediction outputs..."
## [1] 0.45
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final.All.X##rcv#glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## Low.cor.X##rcv#glmnet                  0.5882353       0.5838394
## All.X##rcv#glmnet                      0.5882353       0.5838394
## All.X##rcv#glm                         0.5841785       0.5436235
## Max.cor.Y.rcv.1X1###glmnet             0.5780933       0.5660003
## Interact.High.cor.Y##rcv#glmnet        0.5780933       0.5660003
## Max.cor.Y##rcv#rpart                   0.5780933       0.5440283
## Random###myrandom_classfr              0.5780933       0.5142038
## MFO###myMFO_classfr                    0.5780933       0.5000000
## Final.All.X##rcv#glmnet                       NA              NA
##                                 max.AUCpROC.OOB min.elapsedtime.everything
## Low.cor.X##rcv#glmnet                 0.5370867                     14.978
## All.X##rcv#glmnet                     0.5370867                     15.017
## All.X##rcv#glm                        0.5297993                      6.627
## Max.cor.Y.rcv.1X1###glmnet            0.5332490                      0.807
## Interact.High.cor.Y##rcv#glmnet       0.5326586                      2.243
## Max.cor.Y##rcv#rpart                  0.5326586                      1.549
## Random###myrandom_classfr             0.4988023                      0.397
## MFO###myMFO_classfr                   0.5000000                      0.580
## Final.All.X##rcv#glmnet                      NA                     17.861
##                                 max.Accuracy.fit opt.prob.threshold.fit
## Low.cor.X##rcv#glmnet                  0.6037574                   0.55
## All.X##rcv#glmnet                      0.6037574                   0.55
## All.X##rcv#glm                         0.5445999                   0.50
## Max.cor.Y.rcv.1X1###glmnet             0.6190234                   0.55
## Interact.High.cor.Y##rcv#glmnet        0.6168144                   0.50
## Max.cor.Y##rcv#rpart                   0.6174930                   0.50
## Random###myrandom_classfr              0.5778230                   0.40
## MFO###myMFO_classfr                    0.5778230                   0.40
## Final.All.X##rcv#glmnet                0.5973970                   0.55
##                                 opt.prob.threshold.OOB
## Low.cor.X##rcv#glmnet                             0.45
## All.X##rcv#glmnet                                 0.45
## All.X##rcv#glm                                    0.15
## Max.cor.Y.rcv.1X1###glmnet                        0.40
## Interact.High.cor.Y##rcv#glmnet                   0.40
## Max.cor.Y##rcv#rpart                              0.40
## Random###myrandom_classfr                         0.40
## MFO###myMFO_classfr                               0.40
## Final.All.X##rcv#glmnet                             NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   D   R
##         D  20 188
##         R  15 270
##    err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## No        914.5756        234.7403        1150.642              NA
##    .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## No              1              1              1   1966       33      589
##    .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## No    493     1038     1421    622   1966    622   2459        0.4761466
##    err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## No        0.4651961               NA        0.4679309
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##      914.5755991      234.7402522     1150.6422008               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##        1.0000000        1.0000000        1.0000000     1966.0000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       33.0000000      589.0000000      493.0000000     1038.0000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##     1421.0000000      622.0000000     1966.0000000      622.0000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##     2459.0000000        0.4761466        0.4651961               NA 
## err.abs.trn.mean 
##        0.4679309
## [1] "Features Importance for selected models:"
##                                 All.X..rcv.glmnet.imp
## Hhold.fctrPKn                               100.00000
## Q115611.fctrNo                               74.83104
## Q113181.fctrNo                               71.92175
## Q115611.fctrYes                              70.42909
## Q98869.fctrNo                                66.61290
## Q101163.fctrDad                              62.46552
## Q113181.fctrYes                              55.51710
## Q116881.fctrRight                            49.42789
## Q99480.fctrNo                                43.25342
## Q109244.fctrNo:.clusterid.fctr2              39.55891
## Q98197.fctrYes                               30.28896
## Q120472.fctrScience                          28.69665
## Hhold.fctrPKy                                27.22693
## Q108855.fctrYes!                             20.85451
## Q106272.fctrNo                               20.66949
## Q110740.fctrPC                               20.23804
## Q98197.fctrNo                                16.49085
## YOB.Age.fctr^6                               15.71726
## Hhold.fctrMKy                                15.15250
## Q116953.fctrNo                               14.75715
## Q106997.fctrGr                                0.00000
## Income.fctr.Q                                 0.00000
##                                 Final.All.X..rcv.glmnet.imp
## Hhold.fctrPKn                                    100.000000
## Q115611.fctrNo                                    34.272514
## Q113181.fctrNo                                    58.066800
## Q115611.fctrYes                                   75.958431
## Q98869.fctrNo                                     37.501626
## Q101163.fctrDad                                   41.197966
## Q113181.fctrYes                                   22.193770
## Q116881.fctrRight                                 56.180154
## Q99480.fctrNo                                     23.808496
## Q109244.fctrNo:.clusterid.fctr2                   24.178851
## Q98197.fctrYes                                     0.000000
## Q120472.fctrScience                                1.778150
## Hhold.fctrPKy                                     11.162236
## Q108855.fctrYes!                                  15.054634
## Q106272.fctrNo                                    10.817868
## Q110740.fctrPC                                    22.924082
## Q98197.fctrNo                                     57.850515
## YOB.Age.fctr^6                                     0.000000
## Hhold.fctrMKy                                     18.578708
## Q116953.fctrNo                                     1.214709
## Q106997.fctrGr                                    20.363764
## Income.fctr.Q                                     12.681612
## [1] "glbObsNew prediction stats:"
## 
##   D   R 
##  33 589
##                   label step_major step_minor label_minor     bgn     end
## 22     predict.data.new         10          0           0 466.884 479.128
## 23 display.session.info         11          0           0 479.129      NA
##    elapsed
## 22  12.244
## 23      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 2               inspect.data          2          0           0  13.729
## 13              cluster.data          5          0           0 199.194
## 16                fit.models          8          0           0 302.157
## 17                fit.models          8          1           1 370.651
## 3                 scrub.data          2          1           1 160.470
## 14   partition.data.training          6          0           0 270.858
## 20         fit.data.training          9          0           0 433.297
## 22          predict.data.new         10          0           0 466.884
## 18                fit.models          8          2           2 421.026
## 21         fit.data.training          9          1           1 459.566
## 1                import.data          1          0           0   6.566
## 15           select.features          7          0           0 297.456
## 19                fit.models          8          3           3 430.230
## 11      extract.features.end          3          6           6 197.610
## 12       manage.missing.data          4          0           0 198.544
## 10   extract.features.string          3          5           5 197.543
## 9      extract.features.text          3          4           4 197.486
## 7     extract.features.image          3          2           2 197.396
## 4             transform.data          2          2           2 197.293
## 6  extract.features.datetime          3          1           1 197.356
## 8     extract.features.price          3          3           3 197.450
## 5           extract.features          3          0           0 197.335
##        end elapsed duration
## 2  160.469 146.740  146.740
## 13 270.857  71.664   71.663
## 16 370.650  68.493   68.493
## 17 421.026  50.375   50.375
## 3  197.292  36.822   36.822
## 14 297.455  26.597   26.597
## 20 459.565  26.268   26.268
## 22 479.128  12.244   12.244
## 18 430.229   9.203    9.203
## 21 466.883   7.318    7.317
## 1   13.729   7.163    7.163
## 15 302.156   4.700    4.700
## 19 433.296   3.067    3.066
## 11 198.543   0.933    0.933
## 12 199.193   0.650    0.649
## 10 197.609   0.066    0.066
## 9  197.542   0.056    0.056
## 7  197.449   0.053    0.053
## 4  197.335   0.042    0.042
## 6  197.396   0.040    0.040
## 8  197.485   0.035    0.035
## 5  197.356   0.021    0.021
## [1] "Total Elapsed Time: 479.128 secs"